Enhanced surveillance of subsurface operation integrity using neural network analysis of microseismic data

ABSTRACT

Methods are disclosed for monitoring operation integrity during hydrocarbon production or fluid injection operations. According to the methods, received microseismic data is processed to obtain a plurality of data panels corresponding to microseismic data measured over a predetermined time interval. For each data panel, trigger values are calculated for data traces corresponding to sensor receivers of the microseismic monitoring system. At least one data panel is selected as a triggered data panel that satisfies predetermined triggering criteria. A value is calculated for each of at least two event attributes of a plurality of event attributes of the event. An event is classified into at least one event category of a plurality of event categories based on the event score. Related non-transitory computer usable mediums are also disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-In-Part (CIP) of U.S. patentapplication Ser. No. 16/271,203, filed Feb. 8, 2019, titled “ENHANCEDSURVEILLANCE OF SUBSURFACE OPERATION INTEGRITY USING MICROSEISMIC DATA,”which claims the priority benefit of U.S. Patent Application No.62/649,924 filed Mar. 29, 2018, titled ENHANCED SURVEILLANCE OFSUBSURFACE OPERATION INTEGRITY USING MICROSEISMIC DATA, the entirety ofwhich is incorporated by reference herein.

BACKGROUND Field of Disclosure

The present disclosure relates generally to processes and methods tomonitor the integrity of subsurface operations related to the recoveryof hydrocarbons from, and the injection of fluids and waste materialinto, the subsurface. More specifically, the disclosure relates to usingmicroseismic data to monitor well integrity, geomechanics, and otherparameters during oil and gas production operations.

Description of Related Art

This section is intended to introduce various aspects of the art thatmay be associated with the present disclosure. The discussion is meantto provide a framework to facilitate a better understanding ofparticular aspects of the present disclosure. Accordingly, it should beunderstood that this section should be read in this light, and notnecessarily as an admission of prior art.

In oil and gas production operations, particularly those involvingEnhanced Oil Recovery (EOR) techniques and other types of fluidinjection, human activity may impact stress distribution in thesubsurface. For example, in thermal injection processes such as CyclicSteam Stimulation (CSS), Steam-Assisted Gravity Drainage (SAGD), andvariations of these processes, the high-pressure, high-temperature steaminjected into a reservoir may generate thermal stresses around the welland areas adjacent to the injection site. The overburden may alsoexperience shear stresses due to reservoir dilation resulting from theinjection of steam. Formation dilation and geomechanical stresses mayalso arise from the injection of water, gas, and other fluids, such ascarbon dioxide from carbon capture and storage (CCS) or slurrified wasteinjection operations. In the absence of appropriate risk mitigationmeasures, these stresses may cause casing or liner failures, casing orliner slips, cement micro-fractures or de-bonding, fluid incursions,breaching to surface, and other operation integrity problems. Themicroseismic waves generated from such events can be recorded andanalyzed to evaluate operation integrity. As such, these types of eventsare hereafter referred to as “microseismic” events.

Microseismic events are low-scale seismic disturbances often caused byhuman activity (to be distinguished from large-scale seismic eventsgenerated by earthquakes and other natural causes). Microseismic eventsare typically 10⁴ to 10⁶ times lower in magnitude than the naturalearthquakes that can be felt at the Earth's surface. Because of the linkbetween operation integrity issues and microseismic events, passivemicroseismic monitoring has become an important tool to monitorsubsurface conditions. Passive microseismic monitoring relies onsensitive devices that are able to detect relatively low-intensityseismic waves in the ground. While seismic activity in the subsurfacenear the wellbores may result from multiple factors involving not onlyhuman activity but also natural causes, microseismic monitoringtechnology has evolved to provide sufficiently accurate data for trainedoperators to distinguish and identify potential casing failures,subsurface fractures, and other events threatening the integrity ofproduction operations.

The industry has progressed microseismic monitoring technology since theearly 1990's. R. J. Withers and R. Dart describe a program to evaluate ahydraulic fracture treatment in Seismic Imaging of Cotton ValleyHydraulic Fractures (SEG 1998-0968). This field experiment was alsopresented in Overview: Cotton Valley Hydraulic Fracture Imaging Projectby R. Zinno, J. Gibson, R. N. Walker, and R. J. Withers (SEG 1998-0926).R. J. Withers and S. Rieven describe a permanent monitoring system toevaluate fracture growth of a waste injection operation in FractureDevelopment During Cuttings Injection Determined by Passive SeismicMonitoring (SEG 1996-0106). The relationship of the recorded event datato the source mechanism has been investigated, for example, inSource-Mechanism Studies on Microseismicity Induced by HydraulicFractures (SPE-135254, 2010). Previous publications related to thepresent subject matter include: S. Talebi, M Cote, and R. J. Smith,Microseismic Detection of Casing Failures at a Heavy Oil Operation, U.S.Rock Mechanics Symposium, American Rock Mechanics Association(ARMA-07-208) 2007; T. J. Boone, S. Nechtschein, R. J. Smith, D. Youck,and S. Talebi, Microseismic Monitoring for Fracturing in the ColoradoShales above a Thermal Oil Recovery Operation (ARMA-99-1069); and J. R.Bailey, R. J. Smith, C. M. Keith, K. H. Searles, and L. Wang, PassiveSeismic Data Management and Processing to Monitor Heavy Oil SteamingOperations (SPE-117484, 2008), the contents of all of which areincorporated in their entirety by reference herein.

Throughout these and other applications, a common theme has been thechallenge created by the vast quantity of recorded data generated byseveral dozen sensors recording acoustic data (microseismic data)continuously at 2,000 samples per second, or more. Current microseismicmonitoring systems generate enormous amounts of data that requiremultiple hours of manual review by trained operators each day.Consequently, a need exists for systems and methods to more efficientlyanalyze and classify microseismic data gathered during oil and gasproduction and other injection operations to quickly identify andisolate significant incidents that may affect operations and requirecorrective action.

SUMMARY

In an example, the present disclosure provides a computer-implementedmethod for monitoring operation integrity during hydrocarbon productionor fluid injection operations. The method may comprise detectingmicroseismic waves in a subsurface area of operation using a seismicmonitoring system; receiving, from the seismic monitoring system,microseismic data representative of the microseismic waves; processingthe microseismic data to obtain a plurality of data panels correspondingto microseismic data measured over a predetermined time interval;determining, with a neural network analysis implemented on the computer,whether any of the plurality of data panels includes a noise event or anon-noise event; for data panels including a non-noise event, thencalculating, for each data panel, trigger values for data tracescorresponding to sensor receivers of the microseismic monitoring system;selecting, as a triggered data panel, at least one data panel thatsatisfies predetermined triggering criteria; selecting, as a non-trivialdata panel containing microseismic data representative of an event, atleast one triggered data panel that satisfies spectral density criteria;calculating a value for each of at least two event attributes of aplurality of event attributes of the event; determining an event scorebased on the values of the plurality of event attributes; andclassifying the event into at least one event category of a plurality ofevent categories based on the event score.

According to the disclosed aspects, determining whether any of theplurality of data panels includes a noise event or a non-noise event mayinclude determining a number of data levels to be used in the neuralnetwork analysis, and for each dataset, adjusting a number of datalevels associated with each dataset to match the determined number ofdata levels, wherein each dataset is associated with a respective arrayof seismic receivers. The adjusting may include adding data levels tothe dataset, the added data levels having neutral data values or zeroeddata values therein. Additionally or alternatively, the adjusting mayinclude discarding data levels in the dataset by calculating, for eachtrace of each data level, short-time (STSD) and long-time (LTSD) movingstandard deviations of data in said each trace, calculating STSD-to-LTSDratios (SLR) for each trace, identifying a maximum SLR and a location ofthe maximum SLR for each trace, calculating a frequency threshold foreach trace such that a predetermined portion of energy of said eachtrace is contained below the frequency threshold, and discarding a datalevel if the frequency threshold is below a cutoff frequency for atleast 50% of the traces in the data level. If, after discarding the datalevel, the number of the remaining data levels exceeds the determinednumber of data levels, it is determined which data level of theremaining data levels has a lowest maximum SLR, and said data level isdiscarded. This process is repeated until the number of the remainingdata levels equals the determined number of data levels. It may bedetermined whether a moveout attribute exists for each trace, themoveout attribute being the maximum SLR and its location, and themoveout attribute may be fed into the neural network analysis. Themoveout attribute may also include a maximum STSD value over thestandard deviation of said each trace, and the location of the maximumSTSD value. Additionally, noise spikes may be identified and fed intothe neural network analysis.

The plurality of event attributes may comprise magnitude, proximity,polarity, P/S ratio, and SH/SV ratio. Magnitude in turn may comprise atleast one of peak particle velocity, energy flux, moment flux, and RPPV(Range×Peak Particle Velocity). Further, proximity may comprise at leastone of: distance between event location and sensor receivers, distancebetween event location and offset wellbores, distance between eventlocation and wellbore intervals, distance between event location andreservoir layers, and distance between event location and naturalfractures or faults.

The method may further comprise validating the event classificationusing at least one type of non-seismic operational surveillance data,such as wellhead pressures, injection rates, delta flow-pressure alarms,nitrogen soak trends, wellhead temperature, casing head pressure, casinghead temperature, downhole pressure, downhole temperature, injectionflow rate, and production flow rate. In some embodiments, processing themicroseismic data to obtain a plurality of data panels may comprisedividing the microseismic data in data segments of specified length; anddividing the data segments into the data panels. In yet otherembodiments, the trigger values may be calculated using an STA/LTAanalysis, absolute amplitude thresholding, relative amplitudethresholding, wavelet transform calculations, or a combination thereof.

The predetermined triggering criteria may comprise that at least onedata panel has overlapping triggered time windows for data from at leasttwo sensor receivers; or that at least one data panel has overlappingtriggered time windows for data from at least two sensor receivers andthat at least one of the triggered sensor receivers has at least twotriggered channels. In yet other embodiments, the non-trivial data panelmay be selected using a spectral density selection criteria, such thatthe 90% cumulative spectral density of the data lies below 300 Hz.

In other embodiments, calculating a value for each of at least two eventattributes of a plurality of event attributes may comprise determiningan event location; and using the event location to calculate at leastone of the values. Event location may be determined based on P-wavearrivals on at least two sensor receivers; or P-wave arrivals on atleast two sensor receivers and an S-wave arrival on at least one sensorreceiver. In some examples, event location may be used to calculate aproximity value by determining at least one of a distance between eventlocation and sensor receivers, a distance between event location andoffset wellbores, distance between event location and wellboreintervals, a distance between event location and reservoir layers, anddistance between event location and natural fractures or faults.

In another example, determining an event score may comprise calculatinga score for the at least two event attributes; and combining the scoresfor at least two event attributes. Determining an event score may inturn comprise calculating a magnitude score, a polarity score, aproximity score, an SH/SV score, and a P/S score; and adding themagnitude score, polarity score, proximity score, SH/SV score, and P/Sscore to obtain the event score. The plurality of event categories maycomprise casing failure, casing slip, Continuous Microseismic Radiation(CMR) event, heel event, heave event, cement crack, surface noise, androd noise. In some embodiments, the method may further include adjustingone or more operation parameters of the hydrocarbon production or fluidinjection operations based on the event category. The operationparameters comprise fluid injection rate, fluid injection pressure,nitrogen injection rate, and nitrogen injection pressure. If the eventcategory is a casing failure, a casing integrity check may be performed.

In another example, a method for monitoring operation integrity duringhydrocarbon production or fluid injection operations is provided. Themethod includes: detecting microseismic waves in a subsurface area ofoperation using a seismic monitoring system; receiving, from the seismicmonitoring system, microseismic data representative of the microseismicwaves; processing, with a computer, the microseismic data to obtain aplurality of data panels corresponding to microseismic data measuredover a predetermined time interval; determining, with a neural networkanalysis implemented on the computer, whether any of the plurality ofdata panels includes a noise event or a non-noise event; for data panelsincluding a non-noise event, calculating with the computer, for eachdata panel, trigger values for data traces corresponding to sensorreceivers of the microseismic monitoring system; selecting with thecomputer, as a triggered data panel representative of an event, at leastone data panel that satisfies predetermined triggering criteria;calculating, with the computer, a value for each of at least two eventattributes of a plurality of event attributes of the event; and using,with the computer, a classification algorithm to classify the event intoat least one event category of a plurality of event categories based onthe values.

In still another example, a method for monitoring operation integrityduring hydrocarbon production or fluid injection operations is provided.The method includes: detecting microseismic waves in a subsurface areaof operation using a seismic monitoring system; receiving, from theseismic monitoring system, microseismic data representative of themicroseismic waves; processing, with a computer, the microseismic datato obtain a plurality of data panels corresponding to microseismic datameasured over a predetermined time interval; determining, with a neuralnetwork analysis implemented on the computer, whether any of theplurality of data panels includes a noise event or a non-noise event;for data panels including a non-noise event, calculating with thecomputer, for each data panel, trigger values for data tracescorresponding to sensor receivers of the microseismic monitoring system;selecting with the computer, as a triggered data panel, at least onedata panel that satisfies predetermined triggering criteria; selectingwith the computer, as a non-trivial data panel containing microseismicdata representative of an event, at least one triggered data panel thatsatisfies spectral density criteria; determining, with the computer, anevent location; and using, with the computer, the event location toclassify the event into at least one event category of a plurality ofevent categories.

In yet another example, a computer-implemented method for monitoringoperation integrity during hydrocarbon production or fluid injectionoperations may comprise detecting microseismic waves in a subsurfacearea of operation using a seismic monitoring system; receiving, from theseismic monitoring system, microseismic data representative of themicroseismic waves; processing the microseismic data to obtain aplurality of data panels corresponding to microseismic data measuredover a predetermined time interval; determining, with a neural networkanalysis implemented on the computer, whether any of the plurality ofdata panels includes a noise event or a non-noise event; for data panelsincluding a non-noise event, then calculating, for each data panel,trigger values for data traces corresponding to sensor receivers of themicroseismic monitoring system; selecting, as a triggered data panel, atleast one data panel that satisfies predetermined triggering criteria;selecting, as a non-trivial data panel containing microseismic datarepresentative of an event, at least one triggered data panel thatsatisfies spectral density criteria; calculating a value for each of atleast two event attributes of a plurality of event attributes of theevent; and using a classification algorithm to classify the event intoat least one event category of a plurality of event categories based onthe values. Using the classification algorithm may comprise firsttraining the classification algorithm using a training dataset ofmicroseismic data corresponding to known event types; and next using thetrained classification algorithm and the values for the at least twoevent attributes to classify the event. The classification algorithm maybe a Decision Tree, a Discriminant Analysis, a Support Vector Machine, ak-Nearest Neighbor Classifier, an Ensemble-based classifier, or a NeuralNetwork.

In still another example, a method is provided for identifying an eventduring hydrocarbon production or fluid injection operations usingmicroseismic data. The method includes: detecting microseismic waves ina subsurface area of operation using a seismic monitoring system;receiving, from the seismic monitoring system, microseismic datarepresentative of the microseismic waves; determining, with a neuralnetwork analysis implemented on a computer, whether any portion of themicroseismic data includes a noise event or a non-noise event; and forportions of the microseismic data including a non-noise event,classifying, with the computer, the event into at least one eventcategory of a plurality of event categories. Determining whether anyportion of the microseismic data includes a noise event or a non-noiseevent comprises: determining a number of data levels to be used in theneural network analysis; and for each dataset, adjusting a number ofdata levels associated with each dataset to match the determined numberof data levels; each dataset being associated with a respective array ofseismic receivers; using the computer, determining whether a moveoutattribute exists for each trace, wherein the moveout attribute comprisesthe maximum SLR and the location of the maximum SLR; using the computer,identifying noise spikes; and feeding the moveout attribute and theidentified noise spikes into the neural network analysis.

The foregoing has broadly outlined the features of the presentdisclosure so that the detailed description that follows may be betterunderstood. Additional features will also be described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a simplified diagram of an exemplary monitoring wellassociated with a seismic monitoring system.

FIG. 1B is a simplified diagram of an exemplary configuration of surfacecomponents of a seismic monitoring system.

FIG. 2 is a top view of an exemplary well pad configuration including amonitoring well and twenty production wells.

FIG. 3A shows an exemplary microseismic data segment associated with acasing failure event.

FIG. 3B shows an exemplary microseismic data segment associated with acasing slip event.

FIG. 3C shows an exemplary microseismic data segment associated with acement crack event.

FIG. 3D shows an exemplary microseismic data segment associated with acontinuous microseismic radiation (CMR) event (raw data on the left sideand band-pass filtered data on the right side).

FIG. 3E shows an exemplary microseismic data segment associated with aheel event.

FIG. 3F shows an exemplary microseismic data segment associated with aheave event.

FIG. 3G shows an exemplary microseismic data segment associated with asurface noise event.

FIG. 3H shows an exemplary microseismic data segment associated with rodnoise.

FIG. 3I shows an exemplary microseismic data segment associated with anelectrical noise event.

FIG. 3J shows an exemplary microseismic data segment associated with abad channel event.

FIG. 3K shows an exemplary microseismic data segment associated with awhite noise event.

FIG. 3L shows an exemplary microseismic data segment associated with aslimhole/other event.

FIG. 3M shows an exemplary microseismic data segment associated with a Vevent.

FIG. 3N shows an exemplary microseismic data segment associated with aseismic shot event.

FIG. 3O shows an exemplary microseismic data segment associated with anH₂O event.

FIG. 3P shows an exemplary microseismic data segment associated with aripple event.

FIG. 3Q shows an exemplary microseismic data segment associated with abad cable event.

FIG. 4 is a simplified diagram of an exemplary 3-second data segmentdivided into four overlapping data panels.

FIG. 5 illustrates exemplary triggers for a casing failure data segment.

FIG. 6A and FIG. 6B illustrate exemplary triggers before and after noiseidentification, respectively.

FIGS. 7A-7C illustrate different views of an exemplary production padwith monitoring well and the grid points selected for the geophysicalcalculations to solve for the event source locations. Specifically, FIG.7A presents the Easting-Northing view; FIG. 7B shows the Easting-Depthview; and FIG. 7C provides the Northing-Depth view.

FIG. 8A is a display of the event of FIG. 5 with picked P-wave arrivalsand calculated P and S picks, shown in actual recorded time.

FIG. 8B is a split display showing exemplary trace sets for fivereceiver levels, with P-wave travel time corrections and S-wave traveltime corrections, respectively, illustrated on the left panel and theright panel.

FIG. 8C provides a short time window for the event of FIG. 5 displayedin P-wave corrected time.

FIG. 8D illustrates an example of a hodogram display of the eventcomponents shown in FIG. 8C.

FIG. 9 is an Easting-Northing view of an exemplary pad showing amonitoring well and a 2-D surface constructed through the event sourcelocation.

FIGS. 10A-10D illustrate different components of an event sourcelocation solution method. Specifically, FIG. 10A shows a symmetric errorfunction resulting from travel time differences; FIG. 10B shows anasymmetric error function from the inclination angle differences; FIG.10C shows the error function in plan view resulting from azimuth angledifferences; FIG. 10D is a plan view of the composite (or total) errormap.

FIG. 11 is an exemplary snapshot of stripes calculation outputs with twodistinct windows of triggers, used to evaluate event polarity todetermine P-wave and S-wave arrivals on sensor receivers.

FIG. 12A illustrates an exemplary location identification process for adeep event using P-wave arrival picks only.

FIG. 12B illustrates the exemplary location identification process ofFIG. 12A including an S-wave arrival pick.

FIG. 12C illustrates the difference between the two event locations ofFIG. 12A and FIG. 12B in a map view.

FIG. 13A is an exemplary chart illustrating possible trends in injectionrate and well head pressure for a casing failure event.

FIG. 13B is an exemplary chart illustrating a nitrogen soak trend.

FIG. 14 is a flow chart of a method for monitoring well integrityaccording to some aspects of the disclosure herein.

FIG. 15 is a flow chart of a method that may be used with the methodshown in FIG. 14.

FIG. 16 is the binary noise identification result performed by theneural network.

It should be noted that the figures are merely examples and nolimitations on the scope of the present disclosure are intended thereby.Further, the figures are generally not drawn to scale, but are draftedfor purposes of convenience and clarity in illustrating various aspectsof the disclosure. Certain features and components herein may be shownexaggerated in scale or in schematic form and some details ofconventional elements may not be shown in the interest of clarity andconciseness. In addition, for the sake of clarity, some features notrelevant to the present disclosure may not be shown in the drawings.

DETAILED DESCRIPTION

To promote an understanding of the principles of the disclosure,reference will now be made to the features illustrated in the drawings.No limitation of the scope of the disclosure is hereby intended by useof specific language. Any alterations and further modifications, and anyfurther applications of the principles of the disclosure as describedherein are contemplated as would normally occur to one skilled in theart to which the disclosure relates. When referring to the figuresdescribed herein, the same reference numerals may be referenced inmultiple figures for the sake of simplicity.

Definitions

At the outset, for ease of reference, certain terms used in thisapplication and their meanings as used in this context are set forth. Tothe extent a term used herein is not defined below, it should be giventhe broadest definition persons in the pertinent art have given thatterm as reflected in at least one printed publication or issued patent.Further, the present techniques are not limited by the usage of theterms shown below, as all equivalents, synonyms, new developments, andterms or techniques that serve the same or a similar purpose areconsidered to be within the scope of the present claims.

As one of ordinary skill would appreciate, different persons may referto the same feature or component by different names. This document doesnot intend to distinguish between components or features that differ inname only. The terms “including” and “comprising” are used in anopen-ended fashion, and thus, should be interpreted to mean “including,but not limited to.”

The term “seismic sensor” refers to a device capable of measuring ordetecting seismic vibrations or waves and transmitting a correspondingelectronic signal. Examples include, but are not limited to, geophones(which measure ground velocity) and accelerometers (which measure groundacceleration). Hereafter, the terms “seismic sensor” and “channel” maybe used interchangeably.

The term “sensor receiver” refers to a plurality of seismic sensorsarranged or connected so as to detect seismic vibrations or waves alongmultiple directions. Sensors oriented along at least three coordinateaxes are required to determine the motion in three dimensions. Arecording of three or more traces, or channels, from a seismic sensorreceiver may be referred to as a “trace set”.

The term “seismic sensor array” or “sensor array” refers to a pluralityof seismic sensors or a plurality of sensor receivers arranged in aparticular geometric configuration (e.g., circular, linear, etc.) tooptimize detection range and facilitate locating the source of seismicevents.

The term “P-wave” refers to a type of seismic wave characterized in thatthe direction of wave propagation is the same as the direction ofparticle movement.

The term “S-wave” refers to a type of seismic wave characterized in thatthe direction of wave propagation is perpendicular to the direction ofparticle movement.

The term “data acquisition system” or “DAS” refers to a system capableof receiving and sampling seismic data from sensors or sensor receivers(typically in analog form), converting analog signals into digitalsignals, and receiving and storing the resulting digital signalssuitable for computer processing and analysis.

The term “monitoring well” refers to a wellbore in the ground made bydrilling or inserting a conduit into the subsurface to introduce one ormore seismic sensors, sensor receivers, and/or sensor arrays to monitorseismic activity in the vicinity of a region of interest, such as aplurality of producing wells or injection sites. A monitoring well maybe a dedicated well for the sole purpose of monitoring, or it may havebeen converted temporarily or permanently from a production or injectionwell. In some cases, the monitoring well may concurrently be aproduction or injection well if so designed and constructed.

The term “microseismic event” refers to any source of seismic activityor disturbances detectable by a passive monitoring system. Examplesinclude, but are not limited to, well integrity events such as casingbreaks or failures, casing slips, cement cracks, or continuousmicroseismic radiation (CMR), small harmonic tremors, as well as otherevents surrounding a wellbore or injection site, such as shear-dominatedevents and other surface events. The terms “seismic event” and “acousticevent” may be used interchangeably with the term “microseismic event.”

The articles “the,” “a” and “an” are not necessarily limited to meanonly one, but rather are inclusive and open ended so as to include,optionally, multiple such elements.

The terms “approximately,” “about,” “substantially,” and similar termsare intended to have a broad meaning in harmony with the common andaccepted usage by those of ordinary skill in the art to which thesubject matter of this disclosure pertains. These terms are intended toallow a description of certain features described and claimed withoutrestricting the scope of these features to the precise numeral rangesprovided. Accordingly, these terms should be interpreted as indicatingthat insubstantial or inconsequential modifications or alterations ofthe subject matter described and are considered to be within the scopeof the disclosure.

The phrases “for example,” “as an example,” and/or simply the terms“example” or “exemplary,” when used with reference to one or morecomponents, features, details, structures, methods and/or figuresaccording to the present disclosure, are intended to convey that thedescribed component, feature, detail, structure, method and/or figure isan illustrative, non-exclusive example of components, features, details,structures, methods and/or figures contemplated in the presentdisclosure. Thus, the described component, feature, detail, structure,method and/or figure is not intended to be limiting, required, orexclusive/exhaustive; and other components, features, details,structures, methods and/or figures, including structurally and/orfunctionally similar and/or equivalent components, features, details,structures, methods and/or figures, are also within the scope of thepresent disclosure. Any embodiment or aspect described herein as“exemplary” is not to be construed as preferred or advantageous overother embodiments.

Where two or more ranges are used, any number between or inclusive ofthese ranges is implied.

Microseismic Data Acquisition

Thermal injection and solvent-assisted processes are commonly employedin recovering bitumen and heavy oil from underground reservoirs such asoil sands. For example, a well can be drilled in an oil sand reservoirand steam, hot gas, solvents, or a combination thereof, can be injectedto release the hydrocarbons. In Cyclic Steam Stimulation (CSS), forinstance, steam may be injected into the well at fracturing or belowfracturing pressure and allowed to permeate the reservoir for a periodof time to permit the steam and condensed water to heat the viscous oil.The thinned oil is then pumped out of the well and the cycle repeated.In Steam Assisted Gravity Drainage (SAGD), a plurality of horizontalwells may be drilled in the heavy oil reservoir and high pressure steaminjected into the upper well(s) to reduce the oil viscosity and allow itto drain to lower wellbores for production. Other variants of thermalrecovery processes exist that facilitate recovery of heavy oil from oilsands. Solvent-assisted processes, which rely on solvent injection tomobilize viscous oil instead of thermal means, may also be conducted incycles of injection and production. Both hydrocarbon recovery throughthermal injection and solvent-assisted processes are cost-effective waysto produce heavy oil from oil sands, where the high viscosity of bitumenis an obstacle to economic production. Similarly, produced water andslurrified waste may be injected underground into formations fordisposal, and there is recent interest in sequestering carbon dioxidefrom power plants and other sources with subsequent injection forpermanent disposal. However, steam, water, waste slurries, carbondioxide, and other injectants may alter the physical state of theoverburden and surrounding formations that may affect operationintegrity.

Processes and methods described herein improve the efficiency andreliability of passive seismic monitoring of oil production and fluidinjection operations, including those involving extraction of heavy oiland bitumen from oil sands. Aspects of the automated monitoringprocesses and methods described herein increase the integrity ofrecovery or injection processes by providing early detection ofmicroseismic events of interest including those indicative ofwell-integrity events (e.g. casing failures), as well as providing toolsfor evaluation of undesirable fracturing of the formations above theproduction interval. Whereas alternating injection and production offluids into a reservoir occurs in cyclic production processes, disposalof water, waste, and carbon dioxide may also be conducted asinjection-only operations that may increase pressure underground, andseismic monitoring may further help understand containment of thesefluids. Seismic monitoring of such operations may be used in combinationwith additional monitoring technologies, including but not limited to:surface seismic monitoring; surface deformation monitoring using GPS,tiltmeters, and satellite interferometry; downhole tiltmeter monitoring;downhole pressure recording; downhole formation displacementmeasurement; and combinations thereof.

Specifically, to monitor operation integrity using undergroundseismicity, one or more dedicated monitoring wells may be installed inthe vicinity of producing wells or other injection sites as part of apassive seismic monitoring system. It should be understood that, whilesystems described herein may be referred to as passive seismic systems,the waveforms may be described as microseismic waves because they aremost typically of very low amplitude relative to common terminologyregarding seismic waves. For example, microseismic waves relevant toapplications described herein may have a Richter magnitude of about −1to −3, whereas seismic tremors are typically felt at the surface atabout +3 magnitude, or many orders of magnitude greater thanmicroseismic events. Accordingly, passive seismic in this disclosure mayalso be referred to as microseismic monitoring interchangeably.

A simplified diagram of an exemplary monitoring well installation of amicroseismic monitoring system is illustrated in FIG. 1A. In thisexample, a monitoring well 100 is installed at a production site. Asshown in FIG. 1, the monitoring well 100 may be installed at a centrallocation within a group of producing wells 122, 124, 126, 128, and 130.An array of seismic sensor receivers 104 may be placed within a certaindepth of the monitoring well. In this example, the sensor array 104 islocated at depths spanning the intermediate formation 102 and the rocklayer 106 separating the intermediate formation 102 and a reservoir 108.The seismic sensor receiver array 104 includes 5 sensor receivers, butit should be understood that sensor arrays may include any number ofseismic sensors or sensor receivers, and other wellbore and or surfacegeometries are envisioned within the scope of this invention.

Although individual geophones or accelerometers may be employed,tri-axial receiver arrangements may be preferable to monitor seismicevents in three directions. Specifically, tri-axial geophone andaccelerometer receivers are designed to detect seismic vibrations andtransmit a voltage signal indicative of velocity and acceleration,respectively, of the earth movement, along three axes. With three ormore such sensors arranged in an appropriate pattern, athree-dimensional recording of the wave propagation is feasible. Themonitoring well 100 may preferably include between 5 and 12 tri-axialreceivers comprising 15 to 36 individual sensors, or more, that recordmicroseismic data continuously at about 2,000 to 3,000 samples persecond. Sensors and sensor receivers in the sensor array 104 may belocated at different depths within the monitoring well 100. In someinstances, sensor receivers within the sensor array 104 may be spaceduniformly along the length of the monitoring well 100, typically tens ofmeters apart.

It should be noted, however, that passive monitoring systems may rely onother types of sensors and sensor arrays, including without limitation,fiber-optic sensor strings for distributed acoustic sensing, andhydrophone sensor strings. In addition, sensor systems that record onlyone component of the acoustic signal may be arranged in a differentsensor pattern to be able to locate the source of the acoustic event, asdescribed, for example, in P. Duncan et al., Reservoir CharacterizationUsing Surface Microseismic Monitoring, Geophysics, 75(5) pp.75A139-75A146 (2010), the contents of which are incorporated in theirentirety by reference herein. Deployment of the sensor array 104underground may be implemented in any advantageous geometric patterndesigned to detect passive waves within a specified area and from anydirection.

FIG. 1B is a simplified diagram of surface equipment at the wellhead ofa microseismic monitoring well 100. Sensor receivers may be mounted on acoiled tubing 110 and cemented in place for the purpose of maintainingthe desired position and orientation of the sensor receivers within themonitoring well 100. Alternatively, the sensor receivers may be run on acable and clamped to the wellbore in a retrievable configuration. Thesensor receivers may be connected to instrumentation cables 116 that mayrun to a junction or instrumentation box 118 near the surface of thewell 100. The instrumentation box 118 may be connected or otherwisecommunicate with a computer located near the well 100 or in a controlbuilding nearby. The instrumentation box 118 and the computer constitutethe data acquisition system (DAS) in this example. Both cemented andretrievable arrays may also be equipped with means for running adistributed fiber optic sensing system that may record downholetemperature.

Monitoring wells may be strategically positioned in a variety ofconfigurations suitable for both monitoring producing operations andinjection-only operations. With respect to producing operations, FIG. 2shows a simplified diagram of an exemplary arrangement of producingwells around a monitoring well—i.e., a well “pad.” In this productionoperations example, the well pad 200 may consist of any number ofproducing wells grouped for monitoring purposes. In one embodiment, thenumber of producing wells may range between 10-30 wells depending on thedistance between the wells. The location and path of the monitoring well100 may be designed such that the producing wellbores of the well pad200 are within a distance suitable for signal detection. In someembodiments, this distance may be about 150 meters from the monitoringwell 100, through the intermediate formation intervals or,alternatively, any depth range of interest. This criterion has beenadopted in some embodiments because acoustic event magnitudes of −3 havebeen associated with events of interest and have also been observed at arange up to 150 meters from the monitoring well. Monitoring wells mayvary in size and depth, but are often subvertical and terminate severalmeters above the producing interval which can become hotter than thetemperature limits of the sensors. In some instances, abandonedproduction wells may be used for temporary or permanent monitoringsystems, after appropriate cool down and following an appropriateworkover to prepare the well for monitoring. In other instances,observation wells that are used to monitor subsurface pressure may alsobe used for removable seismic arrays. In large production fields therecan be up to a hundred, or more, monitoring systems installed inproduction pads.

In the exemplary illustration of FIG. 2, well pad 200 includes twentyproducing wells and monitoring well 100 roughly positioned at the centerof the well pad 200. In this instance, the producing wells' wellheadsare arranged at the surface in a pattern indicated by 202. The distancebetween wells at the surface may vary, but typically ranges from 5meters or less to more than 15 meters. The distance between themonitoring well 100 and any given producing well of the well pad 200typically varies with depth. For example, in FIG. 2, the producing wellsincline and diverge away from the monitoring well 100 at increasingdepths. Although illustrated as a symmetrical pattern of 5×4 wells atthe subsurface, it should be understood that seismic monitoring systemscontemplated herein include those designed to monitor well pads andinjection sites of varying geometrical configurations (symmetrical andasymmetrical) and any number of producing wells or injection sites. Inproduction operations, the producing wells may be active or inactive,vertical or non-vertical. It should be noted that in this context thereis no distinction between production and injection wells. Indeed, in theCSS process, each wellbore is used for both production and injection.

The monitoring well 100 may be positioned anywhere within well pad 200,preferably at a position that maximizes monitoring seismic waves fromproducing wells at the well pad 200. In a preferred pad geometry, themonitoring well may be placed at a location such that the azimuth angleto each of the monitored wells is different, to provide a span ofazimuth angles to each of the wellbores through the depth range ofgreatest interest, which may be a formation at an intermediate depth,which has the capacity to contain fluids in the event of incidentalfluid losses. Similar principles are applicable to injection-onlyapplications involving injection site pads.

Upon installing a microseismic monitoring system within a monitoringwell, a controlled set of events may be generated at known locations soas to allow the sensor receivers to be calibrated. In this calibrationprocess, surface and/or downhole acoustic sources may be recorded by themonitoring system. The recorded data may be collected and analyzed todetermine the orientation of the axes of the recording system. Thiscalibration allows the orientation of the acoustic wave to be determinedas it impinges on the receiver system. In the absence of suchcalibration, or if there are less than three orthogonal axes in thereceivers, the arrival time of a microseismic wave may be the onlymeasurement that is available to determine the location of the eventsource, which may be acceptable for some array geometries. However, withthe benefit of a tri-axial receiver array, the angular orientations ofthe P- and S-waves may be determined in horizontal and vertical planes.These orientations are of significant value in algorithms used todetermine the source location.

Data management issues from large-scale networks of microseismicmonitoring systems may be challenging. In some networks, a centralserver may receive data from each well pad or injection site pad andmake the data available for downloading from a simple webpage. Each day,pre-packaged bundles of event data and pad statistics may be downloadedfrom the central server to a server located at the offices of dataanalysts. However, the vast amount of data may require hours of manualsorting and analysis to extract the required information from the data.

Exemplary Microseismic Event Classes

According to some aspects of the present disclosure, the characteristicpattern observed in seismic data resulting from particular acousticevents may be employed to automate and expedite the interpretation ofmicroseismic data. For example, FIG. 3A shows an exemplary seismic datasegment corresponding to a casing failure event, a type ofwell-integrity event often of immediate interest to productionoperations. Casing break events, sometimes also referred to as “casingfailures,” are often characterized by high energy, typically withcomparable distributions of P- and S-wave components at some receiversensors. In particular, with reference to FIG. 3A, the data segment maycomprise five trace sets 302, 304, 306, 308, and 310, each correspondingto data generated by a sensor receiver. In this illustration, eachsensor receiver is a tri-axial receiver comprising three seismicsensors, so each trace set includes three data traces corresponding tothe three orthogonal directions along which the sensors detectmicroseismic waves. In this instance, the data segment is about 550milliseconds long. As can be appreciated from the exemplary datasegment, P- and S-waves may be comparable in energy at certainreceivers, and different at other receivers, according to theorientation of the event and the corresponding acoustic radiationpattern.

An additional aspect that may be relied upon by an automated system todistinguish between events of interest may be the “moveout” patternassociated with the P-wave arrivals on different sensor receivers of asingle array of the monitoring well. For example, FIG. 3A shows amoveout pattern (generally along line 312) observed as a result of acasing failure event. This casing failure event illustrates the convexacoustic event moveout pattern corresponding to wave propagationdescribed by the Eikonal equation in which arrivals at more distantsensors are delayed by the acoustic wave travel time in thecorresponding media. In this case, the P-wave arrival is observed firstby a sensor receiver that is not located at an extreme position withinthe sensor array, thus suggesting the event occurred somewhere withinthe span of the array. The data segment of this example suggests thatthe sensor receiver corresponding to trace set 304 is the closestreceiver to the event and the sensor receiver corresponding to trace set310 is farthest away from the event. Estimates of an event's locationdepend on calculations of travel time that include the speed ofpropagation of the P-wave and S-wave components through each formationinterval, which depend on the formation properties, and the angles ofimpingement of the arrival at each receiver.

An additional aspect that may be relied upon by an automated system toidentify high-energy events, such as casing failures, is a clippedwaveform recorded by the sensor receivers, examples of which areindicated by 314 in FIG. 3A. In heritage microseismic recording systemssuch as 12- or 14-bit systems, the presence of clipping may indicate ahigh-magnitude microseismic event. In one embodiment, the recordinggains of the receivers at different depths may be set with highsensitivity in the intermediate formations and lower sensitivity at thetop and bottom of the array. In this configuration, detection isimproved in the interval of interest and arrival times may bedetermined. Recordings from the receivers at the top and bottom, usinglower amplitude gain values, may not clip, thus providing higherfidelity data for the purpose of determining event incidence angles.However, modern 24-bit systems may not clip for even large microseismicevents for most values of system gains because of the increased dynamicrange of the recording system.

Other microseismic events of interest may be visually characterized bydifferent P-wave and S-wave arrivals, moveout patterns, etc. Forinstance, FIG. 3B shows an exemplary data segment corresponding to a“casing slip” event, caused by a slip of production casing relative tosurface casing. This data segment is approximately 350 milliseconds longand includes 10 trace sets corresponding to data gathered by an array of10 tri-axial receivers. As may be appreciated from FIG. 3B, casing slipevents may be characterized in that their P- and S-energies are roughlyequal at several receivers. The P-wave at several receivers may also beof high frequency. Also, the event location of a casing slip is close toa wellbore and at or above the surface casing shoe depth. Additionally,as indicated by the moveout pattern 316, in this instance the event isclosest to the third sensor receiver from the surface, corresponding totrace set 318. A comparison of the depth of the closest sensor receiverto the surface casing depth may indicate if the event is a surfacecasing slip candidate.

In contrast, cement cracks (cement de-bonding caused by thermalexpansion or contraction from, for example, steaming) typically would becharacterized by a high frequency P-wave arrival with relatively littleenergy distributed in the S-wave component across the array. Forinstance, FIG. 3C shows an exemplary data segment corresponding to a“cement crack” event. This data segment is approximately 400milliseconds long and includes 5 trace sets corresponding to datagathered by an array of 5 tri-axial receivers. As may be appreciatedfrom the figure, the cement crack waveforms include sharp P-wavecomponents 320 and less S-wave energy overall.

As a further example of a type of event of interest in production orinjection operations, FIG. 3D shows two views of microseismic datacorresponding to a Continuous Microseismic Radiation (CMR) event. CMRevents are micro-events caused by fluid release into fractures in theoverburden rock (similar to small harmonic tremors). On the left side ofFIG. 3D is shown the raw data segment, whereas the right side shows aband-pass filtered view of the same data. In both views, the datasegment is approximately 375 milliseconds long and includes 5 trace setscorresponding to data gathered by an array of 5 tri-axial receivers.With reference to the left view, it can be appreciated from the raw datathat CMR events may be characterized by small low-energy waves thatrepeat at irregular intervals. With reference to the right side view, itcan be appreciated that the P-wave moveout pattern 326 may be moreapparent in the filtered waveforms. As such, analysis of CMR events mayadvantageously be performed on filtered data segments instead of the rawdata segments.

In contrast to the foregoing, “heel” events may be characterized by highenergy flux, moderately high frequency content and a long tailed P- orS-wave, or both, as shown in the exemplary signature data segment inFIG. 3E. Heel events may be caused at the curvature of deviated wellsdue to turbulent flow of injected/produced fluid or instabilities causedby sudden changes to injection/production rates, or perhaps by thepositional adjustment of equipment located in the well. The exemplarydata segment of FIG. 3E is approximately 650 milliseconds long andincludes 10 trace sets corresponding to data gathered by an array of 10tri-axial receivers. The moveout pattern 328 of the P-wave arrivalindicates that the event in this example is located closest to thedeepest sensor receiver, and below the span of the sensor receiverarray.

Other events not directly related to well integrity that may also beautomatically detected according to some aspects of the presentdisclosure based on the characteristic pattern observed in seismic datainclude shear-dominated “heave” events. An exemplary data segment for ashear-dominated heave event is shown in FIG. 3F. This data segment isapproximately 350 milliseconds long and includes 5 trace setscorresponding to data gathered by an array of 5 tri-axial receivers.Heave events are often caused by subsurface deformation and shear-slipplanes resulting from poro-elastic effects or fluid movement throughfractures or mechanically weak rocks (for example, swelling, exfoliatingshale). Events that are located in the intermediate depth formation andare not close to wellbores are most likely small heave events. Heaveevents may also be generated by communication with natural fractures andfaults. Seismic data associated with subsurface heave events may bedominated by an S-wave component 330 and a lower energy P-wave componentin comparison, as shown in FIG. 3F.

Finally, surface events such as rig noise, truck movement, calibrationshots, etc. may also be detected by a microseismic monitoring system.Surface events are often characterized by high-energy P-wave componentsand may be detected by shallower sensor receivers prior to the deeperones if the source is close to the monitoring well. For instance, FIG.3G shows an exemplary data segment associated with a surface noiseevent. This data segment is approximately 700 milliseconds long andincludes 10 trace sets corresponding to data gathered by an array of 10tri-axial receivers. As can be appreciated from the moveout pattern 332in this data segment, P-waves associated with surface events may befirst intercepted by shallow sensor receivers located near the surfaceof a monitoring well for source locations near the monitoring well. Notethat more distant source locations, for example those generated duringcalibration check shots, may actually arrive first at a deeper receiver(possibly level two, three, or four) due to ray-bending effects in whichthe shortest travel time is observed for wave propagation downwardsfirst and then laterally through higher velocity formations. Indeed,close inspection shows that the arrival on the first receiver, in theslowest velocity intervals, appears delayed relative to the dominantmoveout based on the other receiver levels.

In contrast, noise from deep in the formation (which may be related toworkover operations, including perforating, on relatively close wells,or possibly from more distant wells for signals comprising refractedwaves) may be first observed by sensor receivers located deeper withinthe monitoring well. This can be seen in FIG. 3H, which illustrates anexemplary data segment corresponding to mechanical rod noise in wellsthat are artificially lifted using rod pumps. This data segment isapproximately 1.1 seconds long and includes 10 trace sets correspondingto data gathered by an array of 10 tri-axial receivers. Rod noise can becaused by the rod as it makes contact with the inner tubing stringduring its motion, and is typically characterized by a repeated patternof high-frequency waveforms. In some instances, the time betweensuccessive arrivals may be close to the time-period of the rod pump, butin other cases the time between waveforms may appear random because of avibrational pattern developed in the rod string. Events seen at only onereceiver level are by definition small because the radiation pattern isnot sufficient to provide much of a response on nearby receivers only ashort distance away, as seen at about 700 milliseconds on receiver level4 in this example.

Additionally, there are also other types of events that may warrantinvestigation. FIG. 3I shows an exemplary data segment corresponding toan electrical noise event, where examples of high-energy electricalnoise spikes are indicated by 334. FIG. 3J shows an exemplary datasegment corresponding to a bad channel event, where examples of badchannel trace sets with harmonic waves are indicated by 336. FIG. 3Kshows an exemplary data segment corresponding to a white noise event,where examples of white noise trace sets without significant informationare indicated by 338. FIG. 3L shows an exemplary data segmentcorresponding to a slimhole/other event, which may be caused by noisefrom slimhole, liner hanger, casing patch, or other sourcesSlimhole/other events have a similar moveout pattern, indicated by 340,as casing failure events or heel events. FIG. 3M shows an exemplary datasegment corresponding to a V event, where a reversed “V” pattern isindicated by 342. FIG. 3N shows an exemplary data segment correspondingto a seismic shot event. FIG. 3O shows an exemplary data segmentcorresponding to an H₂O event. FIG. 3P shows an exemplary data segmentcorresponding to a ripple event. FIG. 3Q shows an exemplary data segmentcorresponding to a bad cable event, where high-energy spikes caused by abad cable are indicated by 344.

Data Processing

According to some aspects of the present disclosure, the microseismicdata obtained by a passive monitoring system may be processed by a DASto convert, if required, analog signals gathered from individual sensorsinto digital signals that are stored and transmitted to a remotecomputer or server. In regard to data storage, data may be stored inphysical or cloud-based servers. Data may also be arranged as a “ringbuffer” that continuously overwrites prior data after a period of time,such as several weeks or months. Such ring buffers may be removed fromthe DAS and provided to data analysts for study as necessary, with afresh ring buffer replacing the one removed from service. Themicroseismic data may be processed by any conventional computer togenerate data segments, which are a compilation of data measured over apredetermined amount of time (for example, 10 seconds). Data segmentsmay be filtered before further processing into smaller data panels, ordivided into data panels prior to filtering and further processing.

In this regard, FIG. 4 shows an exemplary division of a data segmentinto overlapping data panels. The data segment includes 5 data tracesets R1, R2, R3, R4, and R5 collected by five separate sensor receiversof a sensor array over a 3 second time span. In this example, the datasegment is divided into four data panels 402, 404, 406, and 408. Eachdata panel in this example is 1.33 seconds long. The data panels 402,404, 406, and 408 may overlap by about 0.33 seconds, as shown in overlapregions 410. However, the present disclosure contemplates data segmentsof any predetermined length, which may be divided into data panels ofany time duration. In some embodiments, the length of data panels may bebetween 0.5 seconds and 10 seconds. The data panels may further beadjacent or overlapping. The amount of overlap between data panels mayvary depending on their size, and in some embodiments it may be between10% and 50%, more preferably, between 25% and 35%. Alternatively, onlyportions of a data panel may be used by the system, or, in otherapplications, it may be necessary to create additional data panels bycombination of two or more data panels. It should be understood thatthere is no artificial limit to the notion of a data panel, and it is acollection of contiguous data values arranged for convenience forprocessing purposes.

One advantage of generating data panels is that a smaller amount of datamay be analyzed faster. Overlapping of data panels may ensure that allof the required event waveform is captured in one dataset to facilitateanalysis. A second advantage is related to the consistency of calculatedmetrics. Signal-to-noise (S/N) ratio criteria are dependent on thetypical amount of signal and noise in a data panel. The duration of manyevents of interest is typically about 0.5-1.0 seconds. For that reason,S/N criteria may be more differentiating if calculated on data panelsof, for example, 1.5 seconds than if calculated on data panels of 10seconds. In the former case, the event duration may be on the order of25-35% of the record, whereas in the latter case the duration may beless than 5%. Typically, there may be more statistical differentiationobserved in a noisy dataset when the signal is a greater portion of theevent. Accordingly, in some embodiments of the present disclosure, datapanels may preferably have a length of about 1.5 seconds, with anoverlap of about 33%, without being limited to these specific values. Insome applications, such a configuration has certain benefits whencalculating event triggers to determine if there is meaningfulinformation in each data panel.

Differentiating Between Noise Events and Non-Noise Events Using NeuralNetwork Analysis

In an aspect, it may be possible to employ neural network analysis todifferentiate between noise events and non-noise events in data panelsor other subdivisions of the microseismic data. Such differentiation mayeliminate the need to further process the noise events, thereby reducingbuffer storage size as well as processing time.

The goal of this binary noise identification process is to classifyevents detected in the data panels (or other subdivisions of themicroseismic data) into two categories: events with noise attributes(noise events), and events with non-noise attributes (non-noise events).Non-noise events include things such as: casing failure events, casingslip events, cement crack events, heel events, heave events, surfacenoise events, slimhole/other events, V events, seismic shot events, H₂Oevents, ripple events, and bad cable events. Noise events includeelectrical noise events, rod/steam noise events, bad channel events, andwhite noise events. It should be noted that some types of noise events,such as surface noise events, are classified as non-noise events insteadof noise events in this step because they have a clear moveout. Table 1lists different types of events categorized as noise events or non-noiseevents. Table 1 also lists the number of each event type used in anexample herein to show how the noise events and non-noise events can bedifferentiated in the data panels.

TABLE 1 Category Number Event type Number Noise events 2278 Electricalnoise event 1009 Rod/steam noise event 887 Bad channel event 240 Whitenoise event 142 Non-noise 2206 Casing failure event 185 events Casingslip event 227 Cement crack event 209 Heel event 71 Heave event 167Surface noise event 13 Slimhole/other event 24 V event 400 Seismic shotevent 400 H2O event 200 Ripple event 200 Bad cable event 110

A pattern recognition neural network typically requires the same numberof input variables. However, different passive seismic arrays may havedifferent numbers of receivers associated therewith, and the dataset)generated by each seismic array will have a number of data levelscorresponding to the number of receivers in the array. Each data levelhas three traces, as explained previously herein. Therefore, to applythe neural network, data may need to be pre-processed to provide acommon number of data levels between the datasets to be used. In thepre-processing, data with more or less than a determined number of datalevels are converted to have the determined number. For example, if thedetermined number of data levels is eight, datasets with more or lessthan eight data levels are adjusted to have eight data levels. Forexample, for a dataset with five data levels, three data levels havingdata values of zero (or otherwise neutral values) are added to thedataset. In other words, the dataset is padded with enough neutral orzeroed data levels to reach the determined number of data levels. On theother hand, a dataset having ten data levels will have two data levelsdiscarded. According to disclosed aspects, one method of data discardingincludes calculating, for each trace of each data level, short-time(STSD) and long-time (LTSD) moving standard deviations of data over arelatively shorter time period (e.g., 16 milliseconds) and a relativelylonger time period (e.g., 64 milliseconds), respectively. For eachtrace, STSD-to-LTSD ratios (SLR) are calculated, and the maximum SLR andits location in time is identified as the “moveout attribute”. Next, afrequency threshold is calculated for each trace such that 95% (or someother amount) of the total energy of the signal for that trace iscontained below the frequency threshold (this frequency threshold willlater be defined as ω₉₅ based on Eq. 1). Then, if the frequencythreshold is lower than a cutoff frequency (such as 40 Hz) for at least50% (e.g., two out of three) of the traces for one data level, the datalevel is deemed as a trivial level without significant information, andthe data level is discarded. If more than eight data levels still existafter this procedure, the level with the lowest moveout attribute(maximum SLR) is repeatedly discarded until eight data levels remain.

After the pre-processing, the moveout attribute of non-noise events andthe identification of spikes in electrical noise events are the mostheavily weighted attributes in the classification with the neuralnetwork. Spikes in electrical noise events are evaluated in this aspectbecause they account for a sizeable proportion of false casing failurealerts. Identification of other types of noise events for input into theneural network may be performed at this point in the disclosed process.FIGS. 3H-3K show examples of noise events, while non-noise events withclear moveout attributes throughout almost all levels are shown in otherfigures in FIG. 3. As evident from FIG. 3I, the peaks 334 appear atrandom locations throughout all receiver levels.

According to disclosed aspects, the moveout attribute for each trace canbe defined as (a) the maximum SLR and (b) the location of said maximumSLR, as identified in the data pre-processing step. The moveoutattribute is then fed into the neural network. Additionally, for eachtrace the maximum value of the STSD over the standard deviation of thewhole trace, and the location of said maximum STSD value, are extractedand fed into the neural network. This maximum STSD informationcomplements the maximum SLR information. Spikes in electrical noiseevents are then identified. Various methods of spike identification maybe employed. In a disclosed aspect, spike identification may beaccomplished using an ensemble empirical mode decomposition (EEMD)technique as described in Ensemble empirical mode decomposition: Anoise-assisted data analysis method, by Z. Wu and N. E. Huang, Adv.Adapt. Data Anal., vol. 1, no. 1, pp. 1-41, 2009, the entirety of whichis incorporated by reference herein. The EEMD technique is first appliedto each trace of data, and several (e.g., four) intrinsic mode functions(IMF) and one residual are obtained. By using EEMD, high-frequencycomponents of a data trace may be eliminated as IMFs and the high-energylow-frequency electrical spikes may be retained in the residual. In theapplication of EEMD, the noise level may be set between 0 and 0.2 andthe ensemble number between 1 and 100. Then, all local peaks and theprominences of the peaks are extracted from the residual, for example byusing a Matlab built-in function “findpeaks”. From the prominences,values that are more than a threshold factor (e.g., 15 times theinterquartile range above the upper quartile) are selected as outliers.As the locations of the selected outliers may not be but close to thelocations of the electrical spikes, segments (e.g., 25 milliseconds) ofthe original data trace centered at the locations of the selectedoutliers are extracted, and the location of the maximum value of eachsegment is determined as the location of a potential electrical spike.Finally, for each location corresponding to the potential electricalspike, if the original data of a time length (e.g., 12.5 milliseconds)before or after it do not cross zero, one electrical spike isidentified. After that, the number of electrical spikes for each eventis fed into the neural network for the classification. Moreover, if atleast one electrical spike is identified for a data trace, data segmentsbetween the closest zero crossings on each side of the identifiedelectrical spikes are set as values of zero (or otherwise neutralvalues) for the data trace. Then, STSD, LTSD, and the standard deviationof the entire trace are calculated again for each data trace withidentified electrical spikes to update the moveout attribute and itslocation that are fed into the neural network. Additionally, thefrequency threshold for each trace calculated in the data pre-processingprocedure and the frequency corresponding to the highest power spectralenergy and its fraction of the total power energy for each trace arealso fed into the neural network.

In total, if the number of data levels is determined as 8 in the datapre-processing procedure, 169 input variables are fed into the neuralnetwork, which may be normalized and scaled to have a mean of zero and astandard deviation of one. The parameters of the neural network are setto provide a suitable output. For example, the number of hidden layersmay be set to around 5 and the number of neurons in each hidden layermay be set to be around 200. A regularization factor (e.g., 0.9) mayalso be used to prevent the overfitting. As non-noise events are ofsignificant interest and need to be further analyzed, a better accuracymay be required for the identification of them. Thus, an error weight(e.g., 5) may be applied to non-noise events in the training process ofthe neural network. All data are randomly assigned to be in a trainingset, a validation set, and a test set, e.g., with proportions of 60%,20%, and 20%, respectively. In the end, the trained neural network withthe most accurate result may be used for the binary noiseidentification. The classification performed by an exemplary output ofthe neural network is shown in FIG. 16, including confusion matrices forthe training set 1602, the validation set 1604, the test set 1606, andthe combined set 1608. In the confusion matrices, “0” stands for noiseevents and “1” stands for non-noise events. The confusion matrix for thecombined set 1608 shows the neural network has determined there are 2270noise events and 2200 non-noise events. When these numbers are comparedto Table 1, which shows 2278 noise events and 2206 non-noise events wereinput to the disclosed method, it can be seen that an accuracy of 99.7%is obtained for all data.

Microseismic Event Triggering

According to some aspects of the present disclosure, data panelscontaining non-noise events, or events having non-noise attributes, maybe further analyzed to determine if the data triggers one or morepredetermined criteria to be considered to be an event of interest andthus selected for further analysis. Such criteria may include one ormore of the following: peak amplitude (relative or absolute), ratio ofshort-term to long-term average (STA/LTA), wavelet transformcalculations, apparent velocity, number of geophones and/or receiversthat raise a trigger signal, number of overlapped windows of triggeredsignals, comparison of attributes derived from adjacent windows andother methods known to those skilled in the art. In some embodiments, anapparent velocity filter may use the ratio of the distance between tworeceivers and the time difference between common waveform arrivals todetermine if the signal is “admissible” as a potential seismic event. Iftoo slow, i.e., slower than the speed of sound in the surroundingmedium, then the event may be discounted as likely noise. Note that aplane wave arriving at two receivers at the same time would correspondto an infinite apparent velocity. While nearly simultaneous arrivals attwo receivers may indeed occur, this typically does not occur all alonga receiver array. Events that are recorded at all receivers in a stringnearly instantaneously are generally the result of some electrical noisesource.

Trigger values may be calculated for each data panel using an STA/LTAanalysis, amplitude thresholding, wavelet transform calculations, orcombinations thereof. For example, in embodiments using STA/LTA, theanalysis may proceed as follows. For each data trace, denoted by V(t),the STA may be evaluated as a backward-moving average of the trace, witha window length equal to a few tens of milliseconds, for example,between 5 and 30 milliseconds, or more preferably, 10 milliseconds. TheLTA may be similarly evaluated, but with a larger window length,typically hundreds of milliseconds, for instance, between 50 and 300milliseconds, or more specifically, 100 milliseconds. Next, the ratio ofthe above quantities may be computed as the STA/LTA ratio. The times atwhich the STA/LTA ratio exceeds a predetermined threshold may berecorded. In some embodiments, this STA/LTA ratio threshold may bebetween 2 and 5, or more preferably, 3.

Optionally, the above STA/LTA analysis may be performed after filteringthe data traces by removing any extreme frequency components in thetraces. If such filtering is performed, the bounds on allowablefrequencies may be chosen in a conservative fashion so as to not discardany microseismic events of interest. For instance, a band-pass filterwith bounds [50, 750] Hz may be an acceptable choice of a filter forthis purpose. Alternatively, STA/LTA calculations may be preceded bywavelet filtering techniques.

In some embodiments, the STA/LTA analysis may also be augmented by anamplitude thresholding procedure, wherein the times at which theamplitude of the data trace exceeds a configured threshold are alsodetermined. As described next, two types of amplitude thresholding maybe carried out: absolute and relative. In absolute thresholding, thetimes at which the absolute value of the trace amplitude, |V(t)| exceedsa fixed, pre-configured threshold are marked as triggered. The thresholdmay be chosen between 0.5×10⁻⁷ m/s to 3×10⁻⁷ m/s, and preferably about10⁻⁷ m/s.

In contrast, in a relative thresholding procedure, |V(t)| is compared toa suitable statistic of the data trace, denoted here by S(V(t)). S maybe selected, for example, as the mean, median, Root-Mean-Square, or themaximum value of |V(t)| over the entire length of the data panel. Itshould be understood that the above list is not exhaustive, and otherstatistics may be employed depending on the specific case at hand. Thetimes at which the ratio |V(t)|/S(V(t)) exceeds a pre-configuredthreshold are marked as triggered. The value of the pre-configuredthreshold depends on the statistic S chosen. For example, if S is chosenas the mean of |V(t)|, the threshold can be set anywhere between 20% and50%, preferably 33%.

In certain embodiments, if both STA/LTA and amplitude thresholding areperformed, the intersection of the times recorded from both analyses maybe marked as triggered. In other implementations, the union of two ormore triggering algorithms could be selected.

To illustrate the calculation of trigger values, FIG. 5 shows anexemplary data panel corresponding to a casing failure event. This datapanel includes data measured over approximately 500 milliseconds by 5tri-axial sensor receivers generating 3 data traces (or 1 trace set)each. For each data trace in the data panel, a combination of STA/LTAanalysis and amplitude thresholding criteria was used to calculate thetriggers on the trace. In this context, calculating the triggers on atrace may be viewed as determining a binary trigger value at each timeon the data panel—i.e., at any given time, the trigger value is 1 if thedata trace is triggered at that time, or 0 otherwise. An example of sucha triggered portion (hereafter, “triggered window”) is indicated by theheavy solid line 502 on a data trace recorded by the shallowest sensorreceiver. The triggering process is completed once the triggered windowson all data traces of the data panel have been calculated.

According to some aspects of the present disclosure, a sensor receivermay be marked as “triggered” according to any predetermined receivertriggering criteria, such as requiring that at least one trace on thesensor receiver has been triggered. A triggered window of the sensorreceiver may be defined as a time interval for which the sensor receiveris triggered. Next, the data panel as a whole may be marked as a“triggered data panel” if predetermined data panel triggering criteriaare met; otherwise the data panel is not considered to have triggered.For example, one such predetermined data panel triggering criteria maybe that at least two sensor receivers are simultaneously triggered. Itshould be understood that whether a data panel is considered a triggereddata panel or not at a given time may be determined using a variety ofalternative criteria, such as requiring a different number of triggeredreceivers or directly aggregating the triggers on the channels, amongmany others. Furthermore, the triggered time interval may be advanced bya few milliseconds to include precursor seismic data and held as atriggered state for a minimum duration to facilitate data overlap onadjacent receivers that have some amount of moveout (time-shift) of theevent.

For instance, in FIG. 5, the time window indicated by 504 would be anexample of a triggered window on a data panel, obtained using thepredetermined criterion that at least two sensor receivers must besimultaneously triggered and that at least one triggered sensor receiverhas at least two triggered channels. In this implementation, theaddition of the requirement of another channel on at least one of thetriggered receivers ensures that at least one of the horizontal channelsis triggered simultaneous to the other two channels, which maycorrespond to vertically oriented sensors. The triggered times on thedata panel are all potential candidates for the arrival of the P- andS-waves on the sensor receivers. As such, in some aspects of theinvention, the subsequent event analysis and classification may beperformed using only the triggered windows or portions of the datapanel.

In other implementations, the continuous time record may be searched fortriggers and when the trigger occurs, the reference start time of thetriggered data panel may be set a certain amount in advance of thetrigger time so as to preserve a desired portion of data in advance ofthe trigger. This is an alternative to the approach shown in FIG. 4 todetermine data panels. In some instances, records may be selected usingboth methods, one for immediate use and another for archival recording.

In some embodiments, the above triggering calculations may be performedon a standard computer at the well pad, and the triggered event filesmay be transferred to a central location for further processing. Aweb-based server application may be configured to interface to multiplepads for the purpose of bringing all of the data to a central locationthat is accessible to data analysts. At the server, additionalprocessing of the triggered event files may occur, including detectionof events of interest and rejection of noise events. The entire datastream may be stored either in a ring buffer or on a server. The fulldata stream can be continuously or periodically transferred to acomputer where both automated processing and data analysts can processthe time record in its entirety. In this approach, the triggeringanalysis may also be performed on the data analysts' computer, possiblyusing different trigger values, which may provide additional benefits.

Noise Identification

According to some aspects of the present disclosure, once the triggershave been identified on individual data traces, a frequency-basedprocedure may be employed to identify noisy traces in the data panel andsubsequently remove them from the triggered dataset. This procedure canidentify high-frequency noise such as the mechanical noise caused by rodpumped wells as the rods scrape against the production tubing (earlierexemplarily illustrated in FIG. 3H). Among many characteristics that maybe used to identify such noise events, one may observe in FIG. 3H, forexample, that the rod pump noise record shows higher frequency contentthan several of the other data types. In practice, a great many noiseevents may be captured for every event of interest to operations. Theseextraneous data records can be time consuming to transfer and processmanually if they are not removed from further processing. However, theymay be added to a noise log to capture the criteria by which each suchnoise event was discarded. Note that rod noise may in some instancesresemble CMR (as seen by comparing FIG. 3H and FIG. 3D) and thatprocesses to recover CMR may be designed to address this possibility. Inpractice, in the rare chance that continuous such events occur, it isalways possible to test for rod noise or CMR by stopping the rod pumpsfor a short time interval. If the noise ceases, the source must havebeen the rod pumping motion.

To illustrate noise identification according to some embodiments of thepresent disclosure, an exemplary triggered data panel withoutidentifying rod noise is shown in FIG. 6A, and the same data panel isshown in FIG. 6B after removing traces that were identified as noiseusing a frequency-based procedure. While an exemplary procedure will bedescribed herein, it should be understood that the present disclosurecontemplates other known methodologies suitable for achieving similarresults.

In the example illustrated by FIG. 6A and FIG. 6B, a conventional signalprocessing software package was used to compute the spectral densitiesof the time-series of the data traces from the entire data panel, forall the receiver traces (which may also be referred to as channels). Thespectral density of a time-series, denoted by p(ω), describes the powercontained in the signal as a function of frequency, per unit frequency.The normalized cumulative spectrum (denoted by γ(w)) may then becomputed as the integral of the spectral density between a suitablelower bound ω₀ and ω, divided by the total energy of the signal obtainedby integrating the power from ω₀ to ∞ (infinity). Mathematically, thisquantity is described by:

$\begin{matrix}{{\gamma(\omega)} = {\frac{{\int_{\omega_{0}}^{\omega}{{p(\omega)}d\;\omega}}\ }{{\int_{\omega_{0}}^{\infty}{{p(\omega)}d\;\omega}}\ }.}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$where γ(ω) is a continuous, monotonically increasing function of w, andsatisfies the limiting conditions: γ(ω₀)=0, and γ(∞)=1. The value of ω₀depends on frequency characteristics. Typically, ω₀ may be chosen to besmaller than the lowest frequency component of a microseismic signal.For example, ω₀=0 Hz may be a suitable value for the lower bound. Inpractice, a periodogram estimate may be used as a substitute for thespectral density p(ω).

Next, for each receiver trace or channel, the corresponding frequency ωthat satisfies γ(ω₄₅)=0.95 may be computed. In other words, ω₉₅ may becalculated such that 95% of the total energy of the time-series iscontained between ω₀ and ω₉₅. This is an option that might be usefulgiven that the majority of the energy in noisy microseismic data istypically distributed across high frequencies. As such, thecontributions from lower frequencies may be relatively small. Incontrast, events such as casing failures tend to peak at lowerfrequencies in their spectral density, and have less energy spreadacross the higher frequencies. It should be noted that, in this context,“high” and “low” depend on the specifics of the source of the noise, thefrequency response of the seismic sensors, the data acquisition system,and the acoustic attenuation caused by the rock. Moreover, it should beunderstood that using 95% of the total energy as in the foregoingexample is only an exemplary value, and this is not intended to restrictthe scope of the present disclosure. Persons of skill in the art willrecognize that one may instead choose any value representing a majorityof the signal's energy, for example, between 60% and 100%, or, higherthan 90%.

Continuing with a 95% example, every trace where ω₉₅ exceeds acalibrated threshold value ω may be classified as “noisy” while theremaining traces may be classified as possibly containing datapertaining to a microseismic event of interest. The calibrated thresholdvalue ω may be determined in an empirical fashion by analyzing thespectral densities of training data comprising known microseismic eventsand noise events. The value of ω may be chosen so as to achieve reliabledetection of noise while minimizing the misclassification rate in thetraining dataset. In some embodiments, a suitable value of ω* may bebetween 150 Hz and 300 Hz, and for example, 250 Hz.

In the example illustrated in FIG. 6A and FIG. 6B, w was set to 250 Hz,a value chosen based on the analyses of known microseismic and noiseevents. All data traces with ω₉₅>ω* were labeled noisy, and any triggerson that trace discarded. Here, “discarding” a triggered window may beviewed as setting the trigger value to 0 for all times in the triggeredtime window. For example, in FIG. 6A, 602 indicates a triggered windowon a data trace of the third sensor receiver. Upon performing the noiseidentification procedure, all triggered windows (including 602) wereeliminated, as may be appreciated from FIG. 6B. A similar process may berepeated for all traces in a triggered data panel, thereby discardingthe triggers and trigger windows on all noisy traces. Such tracestherefore may not play any role in the subsequent calculations. Then,any remaining triggered traces may be labeled as noise-free traces.

The noise identification process described above assumed that thequantities p(ω) and γ(ω) are computed using the data traces from theentire data panel. This approach is suitable for short data panels,i.e., wherein the frequency characteristics may be assumed to be uniformacross the entire data trace. In other embodiments, the noiseidentification may be performed by placing greater emphasis on thefrequency spectra around the triggered time-windows, and lesser on thetimes far from the triggered windows. This may be accomplished bycomputing Short-Time Fourier Transforms (STFT), spectral densities andnormalized cumulative power spectra of the data trace around eachdistinct triggered window and discarding the triggered windows on thebasis of the aforementioned thresholding procedures. Different types ofwindow functions may be used in STFTs, including, but not limited to,Bartlett window, Hann window, Hamming window etc. As a particular caseof the Short-Time Fourier Transform with a Gaussian weight function, onemay compute the Gabor transform of the data trace, which automaticallyplaces greater weight on local data. The spectral densities and thenormalized cumulative spectra then become functions of time, in additionto frequency. At each triggered time of a given data trace, the localnormalized cumulative spectrum may be used to identify noisy triggers onthe trace. This approach is suitable in cases where the frequencycharacteristics of the data trace are expected to vary with time.

According to some aspects of the present disclosure, after noiseidentification, the trigger windows for each remaining sensor receivermay be computed using any receiver triggering criteria as describedearlier, such as requiring at least one data trace of the sensorreceiver to be triggered. Then, trigger windows may be identified forthe overall data panel according to predetermined data panel triggeringcriteria as described above (e.g., requiring that at least two sensorreceivers be simultaneously triggered). Subsequently, any data panelwith at least one triggered window may be labeled as a “non-trivial datapanel.” It should be understood that the criteria for selecting whatconstitutes a non-trivial data panel may vary and require more than oneremaining triggered trace or other indicators of noise-free triggeredactivity. For example, the receiver triggering criteria may be thatsensor receivers are triggered at all times when at least one of theircorresponding data traces is triggered. The data panel may be considered“non-trivial” whenever at least two sensor receivers are triggered andthat at least one triggered sensor receiver has at least two triggeredchannels, as an example of data panel triggering criteria.

The result of the foregoing noise identification procedure isillustrated in FIG. 6B. In FIG. 6A, the triggered window 604 had beenobtained as a result of receiver and data panel predetermined triggeringcriteria applied without discarding any trace level triggers. Afteridentifying all of the noise traces, the triggered window 604 wascompletely discarded, as indicated by 612 in FIG. 6B.

Prior to event location calculations, it may be determined if the eventis energetic enough to be a particular type of event, such as a casingfailure. This may be accomplished by multiplying the maximum amplitudeof the data (see Eq. 2) from each sensor receiver with a referencedistance, which for example can be the maximum of the distance betweenthe wellheads of producing wells and the passive seismic monitoringwell, in a production operations implementation of the presentdisclosure. The resulting quantity may be an initial estimate of thetotal event energy, also referred to as a Reference RPPV (Range×PeakParticle Velocity). If the product does not exceed a conservativelychosen threshold, then the event may be considered not energetic enoughto be a casing failure, for example.

Event Location Solution

In some embodiments according to the present disclosure, the analysis ofmicroseismic data corresponding to an event of interest may incorporatethe calculation of geometric characteristics, including event location.Determining the location of an event may be performed through aniterative process that begins with an initial location and proceeds toevaluate the results of that location selection and update as needed tominimize the errors between calculated and inferred data. By way ofexample, the following sequence may be employed to estimate a sourcelocation:

-   -   (i) calculate a set of grid points that becomes coarser as the        distance from the monitoring well increases, and optionally        store this table of geometry and travel time data in a cache;    -   (ii) receive event data with trigger data as calculated above,        and rotate the trace set from tool coordinates to an earth-based        reference frame, such as Easting-Northing-Depth coordinates;    -   (iii) determine an initial set of P-wave arrival time picks from        the triggered data panel;    -   (iv) calculate time windows about each pick and estimate a        best-fit azimuth angle in the horizontal plane, using        pre-calculated geometry tables;    -   (v) determine a vertical surface at this best-fit azimuth angle,        comprising a 2-D set of grid points passing through the grid        point closest to the top receiver;    -   (vi) calculate azimuth, inclination, and travel time errors at        the calculated grid points on the 2-D surface, and determine the        grid point on this surface that has minimum sum of errors;    -   (vii) optionally, determine one or more S-wave arrivals and        recalculate the location by minimizing the adjusted errors; and    -   (viii) search surrounding points to identify any grid points        with lower total error, if any, and iterate the search until        current location is a confirmed minimum total error. Optionally,        interpolate grid points to refine the source location estimate        if so desired.        These elements are discussed in more detail below.        Calculating Grid Point Data

In one embodiment, relevant geometric and travel time data may bepre-calculated and maintained in tables in computer storage, which maybe loaded during processing as needed. In some instances, this may bemore efficient than calculating values “on the fly.”

The surroundings of the monitoring well may be spatially discretized in3-D, along any chosen orthonormal coordinate system,Easting-Northing-Depth for instance. The span of the discretizedgeometry may be selected so as to ensure it covers all potential eventlocations that can be detected by the current monitoring well. Thesearch for the event location may be limited to this 3-D grid in oneembodiment that is conserving of processing time at the expense ofcomputer memory consumption. In other embodiments, it may be appropriateto perform all calculations “on the fly” and avoid a discrete grid instorage. In yet other embodiments, the final location may be interpretedfrom calculated grid values so as to minimize an error criterion. Theresolution of discretization may be chosen based on considerations ofthe desired precision of the event location.

One exemplary set of grid points is illustrated in FIGS. 7A-7C, whereinFIG. 7A shows the Easting-Northing plane, FIG. 7B shows theEasting-Depth plane, and FIG. 7C illustrates the Northing-Depth plane.In these figures, the small black dots in the background represent gridpoints, with closer spacing throughout the production pad area. Thesensor receiver array is indicated by 104, and the dark lines are thewellbore locations on the pad.

Based on the range and speeds of wave propagation in the rock medium,the travel time of P-waves and S-waves between every combination ofgrid-point and sensor-receiver may be predetermined in advance of theevent calculation. Storing these values in a cached table providessignificant acceleration of the location solution process.

The P-wave and S-wave travel times between any two points, such as areceiver and a grid point, may be calculated according to any knownmethod. In some embodiments, a simple calculation may involve using astraight raypath between the two coordinates, and the time of travel maybe calculated using an average velocity that is representative of themedia along the travel path. In such instance, the azimuth angle of theraypath projection on the horizontal Easting-Northing plane may simplybe the azimuth angle between source and receiver. Similarly, in thevertical planes Easting-Depth and Northing-Depth, the raypathprojections on these planes may be that of a vector between the twolocations. Although simple, the foregoing approach may be effective andadequate for many scenarios.

However, as the offset distance between the source and the receiverincreases, and as the variability in formation velocities increases(compressive P and/or shear S), the error associated with the approachdescribed above may grow. This is a common problem in seismicprocessing, and existing literature describes more general calculationtechniques to determine the travel time between source and receiver.See, for example, Solution of the Eikonal Equation By aFinite-difference Method by F. Qin, K. B. Olsen, Y. Luo, and G. T.Schuster, SEG-1990-1004, and Finite-difference Solutions of the 3-DEikonal Equation by T. Fei, M. C. Fehler, and S. T. Hildebrand,SEG-1995-1129, both of which are incorporated herein by reference intheir entirety. These methods typically are solutions to the Eikonalequation and Snell's law.

For curved raypaths in complex media, and possibly deviated monitoringwellbores, the azimuth and inclination angles may differ from thecoordinate vector between the source and receiver. Most of thetechniques to calculate travel time along curved raypaths may alsoestimate the angles of impingement at the receiver. Such improvedestimates of both travel times and angle of impingement may be used asrefined estimates of straight path calculations. The error betweenstraight raypath and more refined calculations varies substantiallydepending on various factors of the monitoring configuration, sourcelocation, and formation velocities.

Receive Trace Sets and Rotate to Earth Coordinates

The event data panel may be first processed through the triggercalculations described above. If the data panel is not rejected fornoise, the calculations may proceed with data from the previous step.Determining an event location may then involve rotating the sensor datato earth coordinates, such as Easting-Northing-Depth (“END coordinates”)for instance. Sensor receiver data may be projected onto any preferredorthonormal coordinate system. The sensor receiver orientationinformation required for this rotation may be obtained from thecalibration procedures described above, and the rotations are performedvia coordinate rotations as may be found in many mathematicalreferences. Note that the trigger time calculations pertain also to therotated dataset as this is an equivalent set of data.

As an example, FIG. 8A shows the data segment from FIG. 5 rotated to theEND coordinate system. The data segment comprises five trace setscorresponding to receivers R1, R2, R3, R4, and R5, each with an Eastcomponent, a North component, and a Depth component. For instance, withreference to the trace set corresponding to receiver R1, trace 802corresponds to the East component, trace 804 corresponds to the Northcomponent, and trace 806 corresponds to the Depth component.

Select Initial P-Wave Picks

In some embodiments, the arrival time of the P-waves (or hereafter,“pick”) may be determined on a minimum number of sensor receivers, suchas two or three. For each receiver with a valid trigger on its datapanel, the data amplitude m(t), (or, equivalently, velocity oracceleration) may be calculated according to the following:m(t)=√{square root over (V _(E)(t)² +V _(N)(t)² +V _(D)(t)²)}  (Eq. 2)where, V_(E) V_(N)(t) and V_(D) (t) respectively denote the seismic datatraces within the sensor receiver's data panel in Easting-Northing-Depthfor instance. As previously described, the receiver data may beprojected onto any preferred orthonormal coordinate system to calculatem(t).

According to some embodiments, the next step in the location analysismay involve identifying all the local minima ({tilde over (t)}_(i)) andlocal maxima (t_(i)) of m(t) for each triggered trace set. The firstlocal maxima t_(i)* at which m(t_(i)*) exceeds a configured threshold(either absolute or relative) value may be marked as the arrival time ofthe P-wave on that sensor receiver or trace set. Alternatively, the stepratios of successive local maximum values s(t_(i))=m(t_(i))/m(t_(i−1))may be computed, and the first time t_(i)* for which s(t_(i)*) exceeds aconfigured absolute threshold may be identified and marked as a possiblearrival time of the P-wave. This process is designed to capture themaximum values of each pulse of an event, and it may be repeated for allthe sensor receivers whose data panels have been triggered. For eventswhich do not have significant P energy, this procedure may result in apick of an S-wave arrival time.

Cataloguing the maximum values of each cycle of the event provides areduced data set that captures the arrival times (t_(i)), peak amplitude(m_(i)), amplitude step (s_(i)), and polarity (p_(i)) of each cycle ofacoustic energy, which may be included in a matrix of the form [t_(i),m_(i), s_(i), p_(i)]. In one implementation, polarity may vary between 1(P-like) to 0 (S-like), or alternatively −1 (S-like). Note there isoften a low amount of background signal present when a P-wave arrives,whereas there may be residual P energy present when an S-wave arrives.For this reason, selecting a P pick may be cleaner than selecting an Spick, which may not have data as closely oriented with the SH-SV plane.

The fidelity of event location calculations may depend on the number ofreceivers with valid P-wave arrival time picks. Without a sufficientnumber of valid P-wave picks, the calculations may become error prone,eventually leading to incorrect event classifications. Thus, optionally,after the P-wave arrivals have been computed on the sensor receivers,additional criteria may be used to determine if the number of sensorreceivers is sufficient to warrant further event analysis. If the numberof sensor receivers with valid P-wave arrivals is less than apredetermined value, which may be either absolute (e.g., 3) or relativeto the total number of working sensor receivers (e.g., 30%), the eventmay not be automatically analyzed any further. Such events can be markedfor a manual analysis. If certain criteria are met, such as high energylevel, these events may be designated with high priority for manualreview.

For example, in FIG. 8A, solid vertical lines (such as 810 for R1)indicate the P-wave arrival picks on each trace set. An event locationhas been automatically calculated, and the first set of vertical dottedlines (such as 812 for R1) show the calculated P-wave arrivals for eachtrace set. Although one P-wave arrival pick and one P-wave arrival isshown for each of the five trace sets, it may be difficult todistinguish them in FIG. 8 as they are shown very close together. In anyevent, FIG. 8 also shows a lagging set of dotted vertical lines (such as814 for R1) for the calculated S-wave arrival times. No S-pick valuesare shown.

Determine a Best-Fit Azimuth Angle

The azimuth angle from source to receiver may be best calculated over atime interval of roughly one to a few oscillations. However, the data atthis point includes arrivals that are shifted in time because of thedifferent travel times to each receiver. Simply selecting a common timewindow may yield too much data to calculate azimuth angle. Accordingly,a convenient transformation to help facilitate selection of anappropriate time interval to determine the event azimuth may be tochange from recorded time to “corrected time”, wherein the differentP-wave arrivals may be aligned.

As an example, it may be useful to consider a data segment transformedby shifting the time axes for each receiver level according to thetravel time difference between some reference time and the picked orcalculated arrival at each receiver. These time shifts may beaccomplished using data picks and/or data cross-correlationcalculations. FIG. 8B is a split display showing exemplary data segmentof five trace sets for five receiver levels, where the left panel 820includes P-wave travel time corrections and the right panel 830 includesS-wave travel time corrections. Specifically, in 820, the rotatedP-SH-SV coordinate data is corrected for the P-wave travel time. In 830,the data is corrected for the S-wave travel time. In this example, thereference value to which all traces are shifted is the time of the firstpick; however, any other reference value may be chosen.

In 820, all P picks such as 810 are aligned, and the P arrival data isalso aligned. However, the S arrivals 814 are not aligned. The Sarrivals are said to be under-corrected as the time shift was too smallto line up the S arrivals. On the other hand, in 830, which is correctedfor the S-wave travel times, the S arrivals such as 832 are aligned. Itis evident, however, that the P picks 834 are not aligned. The P pickshave now been over-corrected since the time shifts are too large. Thesetime shifts correct for the moveout of the arriving energy of each ofthe P-waves and S-waves, respectively.

When so aligned, it is possible to examine small windows about the samecorrected time axis across all receivers to examine the orientation ofthe arrivals in the Easting-Northing, Easting-Depth, and Northing-Depthplanes. This is referred to as a “hodogram” analysis. Hodograms are 2-Dplots that depict the microseismic waveforms in phase-space, and theycan be used to determine the directions of arrival of seismic waves ontoeach sensor receiver, the so-called angle of impingement. Specifically,hodograms can be used to infer the azimuth and inclination angles of theevent relative to a sensor receiver. For each sensor-receiver, hodogramsmay be computed by considering a window of time (typically a few tens ofmilliseconds long) spanning the sensor receiver's P or S arrival picks.

In FIG. 8C, data from the P-corrected display 820 is presented alone,with a shorter time window to provide more accurate hodogram analysis.In this example, the time window is 14 milliseconds in length,comprising a few cycles of particle motion. Longer or shorter timewindows may be selected, this value is just one example.

FIG. 8D illustrates an exemplary set of hodograms 860 on data from thefive sensor receivers, R1, R2, R3, R4 and R5, corresponding to the datawindows of FIG. 8C. Rows 862, 864 and 866 show the P-wave hodograms inthe Northing-Easting, Easting-Depth and Northing-Depth planes,respectively, for each sensor receiver R1, R2, R3, R4 and R5. For eachsensor receiver, the arrows marked in the hodograms align with theprincipal component direction of the corresponding waveform. For P-wavehodograms, these directions point directly toward (or away from) thelocation of the event. As may be appreciated from FIG. 8D, the azimuthangle 872 and those along the top row in panel 862 for all sensorreceivers are approximately equal, since the monitoring well in thisexample is nearly vertical. However, in the vertical plane, theinclination angles in panels 864 and 866 (i.e., 874 and 876,respectively) are significantly different across the sensor receiversbecause the array spans a wide vertical region about the event, and theevent is relatively close to the array.

S-wave hodograms may also be plotted for sensor receivers with validS-wave picks. Row 868 illustrates exemplary S-wave hodograms forreceivers R1, R2, R3, R4 and R5, displayed using an S-corrected timeaxis that is a subset of 830. Since the particle motion for S-waves isorthogonal to the direction of wave propagation, the vector 878 pointingto the event location and the receiver should be oriented at leastsomewhat normal to the principal component 879 in the S-hodogram. Thereare several reasons why the S-wave hodogram is not as sharp an indicatorof direction as the P-wave hodogram, but it can provide some degree ofconfirmation that the S-wave arrival is present.

For each sensor receiver, the time window considered for a P-wavehodogram analysis may be iteratively refined so as to obtain therequired sharpness (i.e., a dominant principal component) and thus amore accurate direction of the seismic wave propagation. However, inmany cases this may not be required.

The azimuth and inclination angles resulting from all such hodograms maybe calculated, stored, and used in subsequent calculations. For eachcombination of grid point and sensor receiver with a valid P-pick, thedifference between the calculated azimuth (describe above) and azimuthdetermined from the sensor receiver's hodogram analysis is calculated asthe “azimuth error.” The same is true for inclination errors calculatedalong vertical planes.

Determine a Vertical Surface Along Azimuth Angle

To render a 3-D problem in 2-D, it is beneficial to first determine a2-D surface that includes a convenient reference and the event sourcelocation. For this purpose, one choice is to use the location of thefirst sensor receiver as a reference. This 2-D surface may be determinedby searching in a horizontal plane near the first receiver anddetermining those points with the least azimuthal error as one traversesthe geometric grid points from one side to the other.

FIG. 9 shows an Easting-Northing view of an exemplary pad showing amonitoring well and a 2-D surface constructed through the event sourcelocation. An example of a sub-planar surface is seen in FIG. 9 as thewavy line 902 passing from the Northwest to the Southeast quadrant, withthe monitoring well represented by the square 904 in the center, and theevent location shown by the asterisk 906. The event is located onwellbore 908 shown by the darker line.

Calculate Component Errors and Determine the Grid Point with MinimumTotal Error

For each sensor receiver with a valid P-arrival pick, the differencebetween the modeled P-wave arrival times and the picked P-wave arrivaltimes may be calculated. This yields the initial “P-wave travel timeerror” between grid-points and corresponding sensor-receiver pairs. Thedifferences between measured and calculated azimuth and inclinationangles are also evaluated.

For each grid point, at least the following geometric attributes may becalculated with respect to each sensor receiver:

-   -   Range: The Euclidean distance between the grid point and the        sensor receiver.    -   Azimuth: The angle between the projection of the line-segment        connecting the grid point to the sensor receiver onto the        Easting-Northing plane and a reference horizontal line pointing        north.    -   Inclination: The angles between the line-segment connecting the        grid point to the sensor receiver and its projection onto        reference Easting-Depth and Northing-Depth planes.

According to some aspects of the present disclosure, for everycombination of grid point and sensor receiver with a valid P-arrivalpick, the difference between the calculated inclination angle (describedabove) and the inclination angle determined from the sensor-receiver'shodogram may be determined as the “inclination error.” Then, for eachpair of grid point and sensor receiver, the travel time, azimuth, andinclination errors may be normalized and summed up to compute a “totalerror” for that pair. This summation of errors may be performed byassigning different weights to each error based on the degree ofconfidence in the calculation of that error. Thus, for example, we mightequate 5 degrees of angular error with 1 millisecond of time error,facilitating normalization of the different error types. The grid pointwith the least total error may be marked as the initial solutionlocation for the microseismic event source.

Optionally, in some embodiments, the accuracy of event locationcalculations may be further improved by identifying sensor receiverswith erroneous arrival picks and omitting them from the analysis. Thismay be accomplished automatically or manually, depending in part on theexecution mode of the processing.

Such sensor receivers may be identified in at least one method using thefollowing exemplary procedure. For each sensor receiver, the eventlocation may be determined by ignoring that receiver from thecalculation process (i.e., considering all the sensor receivers exceptthe one under analysis). The event location determined as such may becompared to the location obtained by considering all the sensorreceivers. If the change in the event location is larger than a chosenvalue, the sensor receiver under analysis may be permanently omitted.This process may be successively repeated for all sensor receivers,marking receivers for permanent omission along the way, until the changein event location is acceptable. The event location calculation may berepeated one final time, using only data from those sensor receiversthat have not been permanently omitted from the analysis.

Computational efficiency is gained by the reduction of a 3-D problem to2-D by use of the vertical surface at the determined azimuth angle. Theproblem is reduced to one of calculating the grid point on this surfacewith minimum error as calculated above. Calculation efficiency may beincreased in some embodiments by starting with the errors from theinitialization step. In the nested loop in which the azimuth,inclination, and time errors are combined, when the accumulated errorexceeds the total error at the initial source location or the error froma previous step, that iteration may be terminated since it is no longera candidate for the title of “minimum error”.

Table 2 provides a table of values of picks, calculated values, anderrors between the two to provide an illustration of the errorcalculations that are minimized in this procedure. The “weighteddifference” for the angular contributions assumes a multiplier of 0.2for an equivalence of 5 degrees equal to 1 millisecond. Note that theinclination errors are determined in each of the two vertical planesalong the axes and are then combined with projection multipliers of sineand cosine squared, such that the total inclination angle weight is 1.Either an L1 norm or an L2 norm may be used in this procedure; both areillustrated.

TABLE 2 R1 R2 R3 R4 R5 L1 Error L2 Error Measured Azimuth 301.0 301.0301.0 301.0 301.0 Calculated Azimuth 300.7 285.9 309.7 307.7 298.1Difference 0.3 15.1 −8.7 −6.7 2.9 Weighted Difference 0.1 3.0 −1.7 −1.30.6 6.74 14.28 Measured E-D Inclination −27.5 −56.3 −134.0 −147.0 −159.4Calculated E-D Inclination −32.4 −70.7 −130.4 −154.3 −163.0 Difference4.9 14.4 −3.6 7.3 3.6 Weighted Difference 1.0 2.9 −0.7 1.5 0.7Projection Multiplier = 0.735 0.7 2.1 −0.5 1.1 0.5 4.97 6.71 MeasuredN-D Inclination 17.1 20.6 139.6 154.3 169.9 Calculated N-D Inclination20.9 59.7 144.8 163.9 169.6 Difference −3.8 −39.1 −5.2 −9.6 0.3 WeightedDifference −0.8 −7.8 −1.0 −1.9 0.1 Projection Multiplier = 0.265 −0.2−2.1 −0.3 −0.5 0.0 3.07 4.67 Measured P-wave Arrival 177.5 159.7 163.3180.7 200.0 Calculated P-wave Arrival 175.5 160.3 164.1 181.1 200.4Difference 2.0 −0.6 −0.8 −0.4 −0.4 4.20 5.32 Sum Total Error 19.0 31.0

FIGS. 10A-10D provide an additional view of how these errors aredetermined for one receiver. Specifically, FIG. 10A shows a symmetricerror function resulting from travel time differences; FIG. 10B shows anasymmetric error function from the inclination angle differences; FIG.10C shows the error function in plan view resulting from azimuth angledifferences; FIG. 10D is a plan view of the composite (or total) errormap. The symmetric error function illustrated in FIG. 10A indicates thattime errors constrain the solution in radius and depth but not azimuth.FIG. 10B shows the importance of the inclination errors to constrain thesolution to one side of the well, which neither time (FIG. 10A) norazimuth angle (FIG. 10C) can accomplish. Inclination also provides adepth constraint. FIG. 10C shows that the solution is constrained to the2-D surface described previously.

At this stage in the process, a grid point on a 2-D surface has beenidentified that has a minimum weighted sum of azimuthal, inclination,and time errors, for the current P picks. In one embodiment, no S-wavepicks have been considered thus far.

Determine S-Wave Arrivals as Appropriate

For certain classes of events, the inclusion of one or more S-wavearrival times can provide an important contribution to the eventlocation. To search for S-wave arrivals, polarization analysis may beused in one exemplary method.

A coordinate system that facilitates polarization analysis is one thatis “pointed” at the source location from each receiver. The axisoriented along the direction from the receiver to the source is the “P”axis corresponding to the compressive P-wave. Perpendicular to the Paxis is the “SH” horizontal shear axis located in the horizontal plane,as well as the “SV” vertical shear axis oriented perpendicular to boththe P and SH axes. This may be referred to as the “PS” coordinatesystem, where it is understood that there is a different PS system ateach receiver. Naturally, the better the source location solution andthe implied calculations, the better the data in PS coordinatesrepresents proper separation of P and S acoustic modes.

FIG. 11 is an exemplary illustration of the result of a proceduredescribed to identify the polarity of each pulse of a trace set,referred to as the “stripes” procedure. With respect to R4, for example,the amplitude m(t) of the trace set is indicated by the solid line 1100.The local maximum data amplitudes m(t_(i)) of the energy pulses areindicated by the tick marks 1102 at the local maxima (t_(i), m_(i)) ofthe data amplitude trace 1100, and the corresponding step valuess(t_(i)) are shown as small circles 1112. In one embodiment, the P-wavearrivals on the sensor receivers R1, R2, R3, R4, and R5 are identifiedas the first local maximum values t_(i)* at which m(t_(i)*) exceed apre-configured value. For example, this pre-configured value may beone-third of the maximum value of m(t_(i)) of each trace set. Howeverother values and criteria may be used within this general framework ofassessing the arrival of energy pulses and determining their magnitudeand orientation. Here, 1102 and 1112 refer to the specific arrival timeof the P-wave on receiver R4.

For each such energy pulse, defined as the time interval betweenadjacent minima of m(t), the polarity p(t_(i)) may be calculated. Thispolarity may be determined in a number of different ways. The series ofboxes 1120 are plotted along the same time axis as 1100 to illustratethe polarity of each pulse. The box 1122 is an upwards tick thatindicates more P-like than S-like orientation, whereas the later arrival(indicated by 1104, 1114, and 1124) is marked by a box that ticksdownwards to show more S-like orientation. The heights of the boxes 1122and 1124 are determined by the ratio of P to S magnitude, in this casethe relative root-mean-square (RMS) values determined for each energypulse, and the shade of the box is related to the pulse step sizes(t_(i)) compared to the maximum step size for the time window.Therefore, the magnitude peak 1102 indicates a candidate P arrival, witha high step size 1112 and a dark, positively-oriented polarity box 1122.On the other hand, peak 1104 is a candidate S arrival, with a relativelyhigh step size 1114 and a dark, negatively-oriented polarity box 1124.

In FIG. 11, the locations of the local maxima (t_(i)) on the time seriesare depicted by tick marks on the plotted amplitude curve, such as thoseindicated by 1102 and 1104. The seismic data record for a receiver maythereby be reduced to a smaller set of arrival peak amplitudes,amplitude steps, and the corresponding polarity. This data depopulationto the set [t_(i), m_(i), s_(i), p_(i)] for each receiver may be usedadvantageously by an automated data processing system as many fewerarrival times need be considered. It should be understood that thisillustration is provided as an example only and other methodologiesknown in the art for calculating arrival times on each sensor receivermay be used with other aspects of the disclosure.

Optionally, according to some aspects of the disclosure, in addition toP-wave arrivals, S-wave arrivals may also be determined for each sensorreceiver. An exemplary procedure to detect S-wave arrivals may be asfollows. The local minima ({tilde over (t)}_(i)) and maxima (t_(i)) ofm(t) have been computed as described above. For each trace setcorresponding to a sensor receiver, the first local maxima t_(i)** thatlies in a triggered window following the time window containing theP-wave pick may be identified and considered as a possible S-wavearrival on that sensor receiver or channel. Then the process may berepeated according to predetermined criteria and iterated to converge tothe minimum error. This procedure might be optimal if there issufficient separation between the P and S energy pulses.

The triggering process for S-wave arrivals may also be performed using adifferent criterion from P-wave triggers. For example, in situationswhere STA/LTA criteria does not lead to triggering of the S-wavearrival, one may choose to perform only an amplitude-based thresholdingdisregarding the step value for an interval following the P arrival,looking for the initiation of S-like polarity. In yet other embodiments,the current event solution determined from the P picks may haveassociated (calculated) S arrival times. These times may not be correct,as the calculated times in a data panel wherein only P-wave arrivalshave been picked may be different from the picked times when S-wavearrivals are also picked. When processing an event in manual mode, thismay be recognized by the analyst and remedied by making at least one Spick, which may be enough to move the solution for certain classes ofevents.

Heel events, for example, occur deep in the formation, whereas asolution based only on P arrivals and hodogram analysis often yieldslocations with calculated S arrivals that are early relative to theobserved S-wave times. The initial source location may be estimatedusing the P-wave picks based on methods disclosed herein, or by anyknown alternate approach. For each receiver, a search starting at thelocation of the calculated S pick may be conducted, looking at eitherearlier or later arrivals, but in a preferred embodiment initiallylooking for later arrivals. The calculated energy pulse matrix [t_(i),m_(i), s_(i), p_(i)] may be evaluated to determine the closest, highamplitude S-like peak value as a trial S pick. With one such pick, theevent location may be updated and then the updated values of the energypulse matrix may be compared with the new calculated S-picks. Thisprocess may be iterated to convergence using a sufficiently robustnumber of S-picks, ensuring that calculated S arrivals are pickedreasonably close to corresponding energy pulse arrivals that haveappreciable S-like polarization values (p_(i)˜0). In this process, theratios between successive amplitudes m_(i) and the change inpolarization angles p_(i) may be used to select the arrival pulse thatindicates the arriving S-wave. In yet another implementation, localmaxima following the calculated S-wave arrival may be considered inpriority order determined by the combined step value and polarity changefrom P-like to S-like. With candidate S picks, the location solution canbe calculated and the error assessed. Note there may be greatervariation between calculated and picked S arrivals than the differencein P arrivals, in part the result of azimuthal anisotropy and/or shearwave splitting.

FIGS. 12A-12C illustrate the effect of using S-wave picks in an eventlocation calculation. Specifically, FIG. 12A shows a data panelcorresponding to an exemplary heel event, wherein only the P-wavearrivals have been picked using the procedure described above. In thisexample, a sensor array of the monitoring system includes ten sensorreceivers, each equipped with tri-axial installation of three geophones.The P-wave picks are indicated by solid vertical line segments, such as1202 corresponding to the P-wave pick on the eighth sensor receiver. TheP-wave arrivals at various sensor receivers estimated from travel timecalculations are shown as vertical dotted line segments 1204. Continuingto focus on the eighth receiver as an example, numeral 1206 indicates anexemplary S-wave arrival calculated using travel time estimates based onthe initial location. The P-S time interval 1208 corresponds to thedifference in the calculated arrival times of the P-wave and S-wave atthe receiver. With only the P-wave picks available, the calculation inthis example places the event at a depth of 370 meters and range of 70meters, at the location marked 1220 in FIG. 12C.

Continuing with this example, FIG. 12B illustrates the same data panelwhere, in addition to the P-wave picks, the S-wave pick 1210 on theeighth sensor receiver has been set. The time interval 1212 correspondsto the difference in P- and S-arrivals at this receiver based on theupdated event location. With this additional S-pick considered in theevent location calculations, the event is located at a depth of 430meters and a range of 95 meters, as shown by 1222 in FIG. 12C. As may beappreciated by comparing the time intervals 1208 and 1212, the fact thatthe latter is longer indicates that the event is located further awayfrom the seismic sensors than the solution given by 1208.

In contrast to the example illustrated in FIGS. 12A-12C, it may bedetrimental to include the S-wave picks in the location of events suchas casing failures that occur in the intermediate depths. This isprimarily because solutions based on P arrivals and inclination angleshave been found to be accurate for events located within about 100meters of the monitoring well, and uncertainty in the S-wave velocitymodel is a source of error for these intermediate depths. While, in someinstances, shear wave azimuthal anisotropy may be suspected, thevelocities are not known precisely for all depths. Thus, in someembodiments of the present disclosure, the microseismic analysis mayinclude a step of identifying if the event is shallow, intermediate ordeep, prior to recalculating the event location using S wave arrivals.In one embodiment, a process based on event depth may be used todetermine whether the S-wave arrival times should be included in theevent location calculation.

Search Surrounding Grid Points and Interpolate Grid

In certain circumstances, it may be recognized that the event sourcelocation is close to, but not precisely located on, the 2-D surface thatwas constructed. In certain embodiments, a local search may then beperformed to determine if there is a nearby grid point that has lowertotal error than the point on the grid. If so, then the offset gridpoint with minimum error is presumed to be the source location and the“step around” procedure may be repeated. This process may iterate untilthe grid point with a local minimum error is obtained.

In other embodiments, an interpolation scheme may be used to refine thesource location estimate from the surrounding grid point data.

Event Attributes

According to some aspects of the present disclosure, values for eventattributes of an event reflected in a non-trivial data panel may becalculated. In this process, an event solution (i.e. event location) maybe used to calculate some attribute values, but the location may notnecessarily be required or useful in the calculation of all attributevalues. In this context, an attribute may be any characteristic of themicroseismic data that can be measured or calculated. For example, apeak event velocity of the measured signals may be determined, or theamplitude or magnitude of the event. The event flux or a measure of thecumulative energy in the particular time window being analyzed may alsobe determined. Other attributes include: event polarity (explosive orimplosive); proximity to one or more of the monitored wells; ratio ofP-wave to S-wave peak velocity or cumulative energy; ratio of SH-wave(horizontal S) and SV-wave (vertical S) peak velocity or cumulativeenergy; event depth; frequency spectral characteristics; consistencybetween channels and depth levels; ratio of cumulative amplitudes ofadjacent overlapping time windows within each channel-time offset fromevents related to production operations.

In yet other embodiments, other geometric characteristics may becalculated such as distance between event location and sensor receivers,distance between event location and offset wellbores, distance betweenevent location and reservoir layers, and distance between event locationand natural fractures or faults, may be determined and relied upon asthe basis for further estimation of attributes of an event that form thebasis of an event classification.

In the following paragraphs, some of these attributes are furtherdescribed and their calculation procedure is exemplified. Most of thecalculations may be performed in commercial software packages such asMATLAB®.

Event Magnitude

The event magnitude is an attribute that indicates the strength of amicroseismic event. In a strict usage of the term, “event magnitude” isexpressed in decibels (dB) or in the Richter scale. In the following, weuse the term interchangeably to denote any quantity that is indicativeof the energy of the microseismic event. In particular, event magnitudemay refer to one or more of Peak Particle Velocity (PPV), Energy Flux,Moment Flux, RPPV, or the presence of a “clipped” signature. Each may bedefined as follows:

Peak Particle Velocity (PPV):

For each sensor receiver (indexed by k), the Peak Particle Velocity maybe computed as:PPV^(k)=max_(t∈T) _(P) _(k) {√{square root over (V _(x) ^(k)(t)² +V _(y)^(k)(t)² +V _(z) ^(k)(t)²)}}  (Eq. 3)where V_(x) ^(k)(t), V_(y) ^(k)(t) and V_(z) ^(k)(t) are the velocitycomponents of the trace set recorded by the seismic sensors of thesensor receiver k, in a time-window T_(P) ^(k) that straddles the P-wavearrival. This time-window is typically a few tens of milliseconds induration. The overall peak particle velocity (PPV*) may be computed asthe mean or the maximum of the peak particle velocities across all thefunctioning, noise-free sensor-receivers with valid P-wave arrivalpicks.

Energy Flux:

For each sensor receiver, the P-, SH- and SV-energy fluxes may beevaluated as follows:

$\begin{matrix}{E_{P}^{k} = {\frac{4{\pi\rho\alpha}}{R_{P}^{2}} \times \left( r^{k} \right)^{2} \times {\int_{T_{P}^{k}}{{V_{P}(t)}^{2}{dt}}}}} & \left( {{Eq}.\mspace{14mu} 4} \right) \\{E_{SH}^{k} = {\frac{4{\pi\rho\beta}}{R_{SH}^{2}} \times \left( r^{k} \right)^{2} \times {\int_{T_{S}^{k}}{{V_{SH}(t)}^{2}{dt}}}}} & \left( {{Eq}.\mspace{14mu} 5} \right) \\{E_{SV}^{k} = {\frac{4{\pi\rho\beta}}{R_{SV}^{2}} \times \left( r^{k} \right)^{2} \times {\int_{T_{S}^{k}}{{V_{SV}(t)}^{2}{dt}}}}} & \left( {{Eq}.\mspace{14mu} 6} \right)\end{matrix}$where ρ is the rock density, α and β are the speeds of propagation ofthe P-waves and S-waves in the rock, r^(k) is the distance between thesensor receiver (k) and the event source; R_(P), R_(SH) and R_(SV) arethe P-, SH- and SV-radiation pattern coefficients respectively (see,e.g., S. Talebi et al., Microseismic Detection of Casing Failures at aHeavy Oil Operation, U.S. Rock Mechanics Symposium, American RockMechanics Association (ARMA-07-208) 2007, incorporated herein byreference); T_(P) ^(k) is a window of time (typically tens ofmilliseconds long) that encompasses the P-wave signal, and T_(S) ^(k) isa time-window (typically larger than the P-window, but still severaltens of milliseconds long) that encompasses the S-wave; V_(P)(t),V_(SH)(t) and V_(SV)(t) are respectively the sensor receiver velocitycomponents along the direction of wave-propagation (P-), along the shearplane in a direction where the particle displacement is parallel to thestrata (SH-), and along the shear plane in a direction normal to thestrata (SV-).

Moment Flux:

For each sensor receiver (indexed by k), the P-, SH- and SV-momentfluxes may be evaluated as follows:

$\begin{matrix}{M_{P}^{k} = {\frac{4{\pi\rho\alpha}^{3}}{R_{P}} \times r^{k} \times {\int_{T_{P}^{k}}{{U_{P}(t)}{dt}}}}} & \left( {{Eq}.\mspace{14mu} 7} \right) \\{M_{SH}^{k} = {\frac{4{\pi\rho\beta}^{3}}{R_{SH}} \times r^{k} \times {\int_{T_{S}^{k}}{{U_{SH}(t)}{dt}}}}} & \left( {{Eq}.\mspace{14mu} 8} \right) \\{M_{SV}^{k} = {\frac{4{\pi\rho\beta}^{3}}{R_{SV}} \times r^{k} \times {\int_{T_{S}^{k}}{{U_{SV}(t)}{dt}}}}} & \left( {{Eq}.\mspace{14mu} 9} \right)\end{matrix}$where U_(P)(t), U_(SH)(t) and U_(SV) (t) are the far-field displacementcomponents at the event source along the P-, SH- and SV-directionsrespectively (remaining variables described above).

RPPV*:

The RPPV attribute for each sensor-receiver, denoted by RPPV^(k), may becalculated as:RPPV^(k) =r ^(k)×PPV^(k)  (Eq. 10)the overall RPPV, denoted by RPPV* may be calculated as the maximum orthe average RPPV of all the sensor-receivers.

Clipped Signature:

In certain microseismic data acquisition systems, particularly 12 or14-bit systems, high energy microseismic events may lead to particlevelocities (or accelerations) that are higher than the calibrated rangeof the sensor receivers. When the waveforms of such events arrive at thesensor receivers, the data collected by the receivers may plateau at theupper and/or lower bound of the design range. In FIG. 3A, for instance,an example of such a plateau on the receiver 304 is indicated by 314.The presence of such plateaus, hereafter “clipped signatures”, isanother indicator of a high magnitude event. A sensor receiver may bemarked as “clipped” if at least one of its seismic sensors has aclipping signature near the P-wave arrival. The fraction of clippedsensor receivers is evaluated as the ratio of the number of clippedsensor receivers to the total number of sensor-receivers with a validP-wave arrival pick.

Polarity (or Percentage of Explosive Sensor Receivers)

Event polarity is an attribute that indicates whether the microseismicevent is implosive or explosive in nature. In certain embodiments,polarity may be defined as the fraction of sensor receivers with anexplosive first motion of the P-wave arrival, as compared to the totalnumber of working sensor-receivers.

Proximity

The proximity attribute may be evaluated as the minimum distance betweenthe calculated event location and the candidate wells on which amicroseismic event may occur. Proximity may also include calculating adistance between event location and sensor receivers, a distance betweenevent location and offset wellbores, distance between event location andwellbore intervals, the event depth d*, a distance between eventlocation and reservoir layers, and a distance between event location andnatural fractures or faults. Here, wellbore intervals of interest mayinclude perforation locations and wellbore equipment (such as packers,sliding sleeves, casing joints, casing shoes, screens, etc.).

Horizontal Vs. Vertical Shear (SH/SV Ratio)

The velocities recorded by the seismic sensors of each sensor-receivermay be projected onto a 3-D coordinate system aligned along the P-, SH-and SV-directions. For each sensor receiver, the ratio of its velocitiesalong the SH and SV directions yields the SH/SV ratio:

$\begin{matrix}{{{SH}/{SV}^{k}} = \frac{\max_{t \in T_{S}^{k}}\left\{ {{V_{SH}(t)}} \right\}}{\max_{t \in T_{S}^{k}}\left\{ {{V_{SV}(t)}} \right\}}} & \left( {{Eq}.\mspace{14mu} 11} \right)\end{matrix}$the overall SH/SV ratio, denoted by SH/SV*, may be evaluated as theaverage or maximum of the SH/SV values of selected sensor receivers.P Vs. S Amplitude (P/S Ratio)

For any sensor receiver, the ratio of the Peak Particle Velocitycomponents along the direction of wave propagation (P-) and the shearplane (S-) yields the P/S ratio for that sensor-receiver:

$\begin{matrix}{{P/S^{k}} = \frac{\max_{t \in T_{P}^{k}}\left\{ {{V_{P}(t)}} \right\}}{\sqrt{{\max_{t \in T_{S}^{k}}\left\{ {V_{SH}(t)}^{2} \right\}} + {\max_{t \in T_{S}^{k}}\left\{ {V_{SV}(t)}^{2} \right\}}}}} & \left( {{Eq}.\mspace{14mu} 12} \right)\end{matrix}$an overall P/S ratio, denoted by P/S*, may be computed as the mean ormaximum of the P/S ratios of selected sensor-receivers.

The above discussion is intended as a summary of various eventattributes that may be associated with particular events of interest andmay be evaluated according to the description above, but it should beunderstood that the present disclosure contemplates other physicalattributes of microseismic events and data that may be quantified and/orassessed according to techniques and methods known in the art.Embodiments described herein are intended to incorporate the evaluationof characteristic attributes of seismic data that may assist in theidentification and categorization of particular events of interest. Inaddition, the methodologies described above for quantifying andassessing various attributes are exemplary and not intended to excludefrom the present disclosure other applicable approaches known in theart.

Point-Based Classification

According to additional aspects of the present disclosure, in someembodiments, a microseismic event may be classified by using apoints-based system that assigns a total score to the event on the basisof one or more of the event attributes described above. The total scoremay be evaluated as the sum of the scores from each of the attributesselected as the basis for an event classification, or any othermathematical combination. As an illustration, an exemplaryclassification using a points-based system may be implemented asfollows.

Magnitude Score:

The magnitude score may be a number assigned to one of theaforementioned attributes that indicate the event strength. For example,if RPPV is used as the attribute, the magnitude score may be based onRPPV* (the overall RPPV value). In general, the higher the RPPV*, thehigher the score that may be assigned. If the primary objective of theclassifier is to detect high-energy microseismic events such as casingfailures, negative scores may also be assigned as a penalty for eventswith RPPV* lower than a conservatively chosen threshold. As an example,a preferred score range for RPPV* may be as shown in Table 3:

TABLE 3 RPPV* Ranges (m²/s) RPPV Score  >10⁻³ 12 (3 × 10⁻⁴, 10⁻³ ] 9(10⁻⁴, 3 × 10⁻⁴] 0 ≤10⁻⁴ −20

Alternately, or in addition to the above, a “clipping score” may beassigned to the event if there is at least one clipped sensor receiver,or if the fraction of clipped sensor receivers exceeds a threshold.Generally, the higher the number of clipped sensor receivers, the higherthe score assigned to the event should be. For instance, this score maybe assigned as follows: if the fraction of clipped sensor receiversexceeds 0.5, 8 points may be assigned, and if the fraction lies between0.3 and 0.5, 4 points may be assigned. For other values, 0 points may beassigned. Similar to the above, a magnitude score may be assigned to themoment and energy flux attributes, if they serve as suitable indicatorsof the event magnitude.

Polarity Score:

If a primary objective of the classifier is to detect explosivemicroseismic events, such as casing failures, an event polarity scoremay be assigned on the basis of the percentage of explosivesensor-receivers. This score may also depend on the number of workingsensor-receivers in the monitoring well. As an example, the followingscore ranges shown in Table 4 may be used:

TABLE 4 No. of Sensor Receivers % Explosive Range Polarity Score ≥5 >80% 10 (70%, 80%] 7 (55%, 70%] 2 ≤55% 0 <5  >80% 10 (67%, 80%] 7 (60%,67%] 2 ≤60% 0

Proximity Score:

A proximity score may be assigned on the basis of the proximityattribute. In general, the closer the event is to an active well, theproximity score may be higher. Negative proximity scores may also beassigned to events that are located far from active wells. In addition,the proximity score may also depend on the number of working sensorreceivers. For instance, an exemplary score range for this attribute maybe as indicated in Table 5:

TABLE 5 No. of Sensor-Receivers Proximity (m) Proximity Score >5 ≤20 m 5(20 m, 40 m] 0  >40 m −20 ≤5 ≤35 m 5 (35 m, 70 m] 0  >70 m −20

SH/SV Score:

An SH/SV score may be assigned on the basis of SH/SV* (the overall SH/SVattribute). When the primary objective of the classifier is to detectcasing failures, the SH/SV score may be low, since it is not a clearindicator of casing failures. As an example, an SH/SV score may beassigned as follows: if the microseismic data is recorded in a formathigher than 14-bit, 5 points may be assigned if SH/SV*<0.5 and 3 pointsassigned if 0.5≤SH/SV*<1. For data recorded with 14-bit systems, theSH/SV term is not used because casing failure events typically clip whenrecorded with 14-bit systems, rendering this metric unable to bedetermined.

P/S Score:

Finally, a P/S score may assigned on the basis of the P/S* (the overallP/S ratio attribute). An exemplary range for this score, when theprimary objective is to identify casing failures, may be as follows: ifthe microseismic data is recorded using a 24-bit system, 3 points may beassigned if 0.3<P/S*<2.5, and 0 points assigned if P/S* lies outsidethis window. For data recorded with 14-bit systems, the P/S score isunused because the signal is likely clipped.

It should be understood that the above exemplary score ranges areprovided for illustration only and the present disclosure contemplatesany scoring methodology that generally ranks attributes and assigns“points” or other metric according to the principles explained herein.The points assigned to attribute values may be based at least on twoconsiderations: (i) the likelihood that the microseismic event didoccur, based solely on the observed value of the attribute, and (ii) theaccuracy with which the attribute itself may be calculated or measured.For example, if the objective of the classifier is to detect casingfailures, the following principles may be relied on:

Magnitude:

Casing failures tend to be very energetic. As such, events with a lowmagnitude (e.g., low RPPV* value) are unlikely to be casing failures.Thus, the magnitude score for such events may also be low or evennegative as a penalty. On the other hand, since a high magnitude is alsoa strong indicator of a casing failure, a high magnitude score may beassigned if the selected magnitude attribute is large.

Proximity:

Casing events must always be located near a wellbore. As such, if anevent locates far away from a well, it is very unlikely to be a casingfailure. Thus, events located far from all wells may be assigned low oreven negative points as a penalty for the Proximity score. On the otherhand, an event located near a well does not strongly imply that theevent is necessarily a casing failure. As such, for such events, amedium-high score may be assigned. The proximity cutoffs may be lower ifthere are more than 5 receivers since the event location is likely to bemore accurate with additional receivers at closer spacing and 24-bitrecording.

Polarity:

Casing events are also explosive in nature. Thus, a medium-high polarityscore may be assigned when a large fraction of sensor receivers hasexplosive first motion signatures. However, low fractions do not ruleout casing failures, so the least polarity score may be greater than theleast proximity score in certain embodiments.

SH/SV Ratio:

Since SH/SV is not a defining attribute of a casing failure, in someembodiments, the range of scores for SH/SV may be lower than the rangeassigned to the Proximity and RPPV attributes. It may not be assigned ascore if there are uncertainties in the accuracy of the SH/SVcalculation itself.

P/S Ratio:

Similar to SH/SV, the range of scores assigned to P/S may also besmaller than the range assigned to Proximity and RPPV attributes. It maynot be assigned a score if there are uncertainties in the accuracy ofthe P/S calculation itself.

Total Event Score:

In some embodiments according to the present disclosure, a “total eventscore” may be computed on the basis of at least two of: a magnitudescore (e.g. RPPV*), a polarity score, a proximity score, a SH/SV score,and a P/S score described above. For example, a point-basedclassification system designed with a primary objective of identifyingcasing failures may rely on higher overall scores as indicators of ahigher degree of certainty that a detected event is caused by a casingfailure. For instance, using score ranges described above, any eventwith an overall score of 27 points may be classified as casing failure.

Further granularity may be incorporated into this basic classificationframework by including additional attributes in the analysis. Forexample, the event depth (d*) may be used to rule out a wide range ofmicroseismic events, without requiring the calculation of other eventattributes. For instance, when d* is sufficiently low, preferably <70 m,the event may be classified as a surface event, likely caused byoperational activities on the ground. In other embodiments, a d* valuethat is too high may indicate a deep microseismic event, which may notwarrant immediate attention. In yet other embodiments, instead of or inaddition to event depth, Delta Flow-Pressure (DFP) alarms may beincluded as an additional attribute. For example, if the microseismicevent is accompanied by a DFP alarm, the event may indicate a loss ofwellbore integrity.

Finally, an event may be classified on the basis of the total scorealone or in combination with additional attributes as described above.Classifier tools contemplated herein include manual or computer-basedalgorithms or programs that enable identification to which of a set ofcategories a new observation belongs, based on a training set of datacontaining observations (or instances) whose category membership isknown. Classifiers are examples of a broader class of “patternrecognition” tools, an actively researched area of supervised machinelearning and artificial intelligence. The task of “training” aclassifier refers to running a chosen machine learning model with a dataset of observations or features whose labels are known, and iterativelyadjusting the parameters of the model so that the predictions of themodel match the labels assigned to the training data set. A secondvalidation data set may also be utilized to test the trained model andfurther adjust the model parameters to obtain good predictiveperformance.

A qualitative likelihood of the event being caused by a casing failuremay be assigned in the following exemplary fashion: events with a totalscore between 27 and 30 points may be labeled as “Low Probability CasingFailures,” those with a total score between 30 and 32 points may beclassified as “Medium Probability Casing Failures,” and those with atotal score higher than 32 points may be classified as “High ProbabilityCasing Failure.” As another example, for situations where theproducer/injector wells near the located event are equipped with surfaceor conductor casing(s), a total event score exceeding a predeterminedvalue (e.g., 27 points), with an event depth d* of less than the maximumsurface or conductor casing depth, may indicate that the event may beclassified as a casing slip. As yet an additional example, in situationswhere the total event score exceeds a predetermined value (e.g., 27points) and the wellhead pressures of the candidate injection wellsindicate a high-pressure injection ongoing at the time of the event, theevent may be classified as a “High Pressure Casing Failure.” In othersituations, the event may be classified as a “Low Pressure CasingFailure.” It should be understood that the foregoing are not anexclusive list of possible classification outcomes based on a totalscore and/or additional attributes, and many permutations are possibleand contemplated herein on the basis of the general principles that havebeen described.

In addition, in alternative embodiments, other classificationmethodologies may be utilized to classify a detected microseismic eventinto categories, including at least one from the following: casingfailure, cement crack, heave event, heel event, CMR, surface event, rodnoise, electrical noise, wellbore noise. For example, a standardclassification algorithm may be employed using a software package suchas MATLAB® or Python to identify events on the basis of at least one ofPPV, energy flux, moment flux, RPPV*, percentage of explosive sensorreceivers, proximity, SH/SV ratio, P/S ratio, fraction of clipped sensorreceivers, and other attributes described above. The classificationalgorithm may be, for example, a Decision Tree, a Discriminant Analysis(linear or polynomial), a Support Vector Machine, a k-Nearest NeighborClassifier, an Ensemble-based classifier, or a Neural Network. Theclassifier may be trained using a training dataset of labeledmicroseismic events with an appropriately chosen cross-validationparameter to avoid over-fitting. The classifier can also be trainedusing toolboxes offered by software packages such as MATLAB® or Python,or other machine-learning algorithms known in the art. Once trained, theclassifier can be utilized in real-time to classify events into one ofthe above categories.

Non-Seismic Data

In some embodiments according to the present disclosure, operational(i.e. non-microseismic) surveillance data, such as wellhead pressures ortemperatures, injection or production flow rates, Delta Flow-Pressurealarms, nitrogen soak alarms etc., may optionally be utilized tooptimize microseismic event classification and enhance casing integritymonitoring. These quantities are described next.

Wellhead Pressures/Temperatures:

The pressures and/or temperatures measured in a window of timeencompassing the microseismic event at the wellheads of injector and/orproducer wells near the calculated event location.

Injection/Production History:

The fluid injection/production rates measured over a window of timeencompassing the microseismic event. Overall history or changes in thewell status may contribute to determining a classification for an event,for example time relative to a steaming cycle.

Delta Flow-Pressure (DFP) Alarms:

The “Delta Flow-Pressure” method comprises continuously recording wellhead pressures and injection rates and monitoring their relative trends.For example, in a situation where the well head pressure begins torapidly drop and the injection rate simultaneously starts increasing, awellbore integrity issue may be suspected and an alarm condition occurs.An example of this situation is illustrated in FIG. 13A, which providesa chart showing the injection rate and well head pressure over time. Aswellhead pressure (indicated by line 1302) goes down and injection rate(indicated by line 1304) goes up, a DFP alarm may be raised forpotential loss of integrity around the time indicated by area 1306.

Nitrogen Soak Alarms:

In the soaking phase of a CSS cycle, nitrogen (or a fluid of higherdensity than steam) may be injected into the wellbore to stabilize thewellbore and prevent the reservoir pushing back the steam injectedduring the steaming phase. In some cases, the nitrogen levels in thewell may drop or become unstable with a downward trend. In such cases, awell integrity issue may be suspected. FIG. 13B is an exemplaryillustration of this phenomenon. The well head pressure 1310 begins torapidly drop around the time indicated by 1312. At this time, a nitrogensoak alarm may be raised for a potential loss in casing integrity.

In addition to the above, other non-seismic operational data such aswell head pressures or temperatures and downhole pressures ortemperatures may also be used to validate the microseismic eventclassification.

In one example, operational surveillance data may be utilized inconjunction with the microseismic event attributes calculated after theevent has been located. As such, in this process, the continuouslyrecorded microseismic data may be analyzed to locate the event, and theoperational surveillance data may play a secondary role, such as inclassifying the event, or to validate the classification. For instance,the microseismic signatures of heel events, caused by sharp changes tothe injection or production rates in a well, tend to be very similar todeep casing failures. Thus, it may not always be possible to distinguishthese two types of events using only microseismic data. In this case,the classification may rely on non-seismic attributes such as the wellhead pressure or temperature in a window of time encompassing the event,the injection rate into, and/or production rate from the candidatewells. If, prior to the event, there is a sharp change in the well headpressure within a threshold level, the event may be classified as a heelevent instead of a deep casing failure. Alternatively, if there is asharp drop in the well head pressure that exceeds the threshold level,the event may be interpreted as a casing failure.

As another example, the operational surveillance data may be utilized asthe primary mode of surveillance. In this approach, triggers may begenerated when anomalies in production or injection states are indicatedby such data. For instance, a delta flow-pressure alarm may be triggereddue to a sudden drop in well head pressure. This trigger may be used toinitiate further processing steps that involve retrieving themicroseismic data collected by the seismic sensors and sensor receiversin a window of time around the time of the alarm, and performing amicroseismic analysis to identify if a microseismic event occurred inthis window. If a seismic event did occur, the location of the event mayhelp narrow down the potential causes of the anomaly.

Applications of Event Classification

Once a microseismic event is classified, the classification of the eventmay drive one or more changes to the production and/or injectionoperations. For example, one or more of the following actions may betaken: discontinuing injection into or production from the identifiedwellbore, injecting weighted fluid into the wellbore, injecting nitrogeninto the wellbore, temporarily shutting in the wellbore, abandoning thewell entirely, reducing steam injection rate or pressure, isolating thecasing and continuing production within tubing to reduce pressure in thereservoir and avoid feeding the compromised pipe with reservoir fluids,monitoring the selected wellbore with additional diagnostic tools (e.g.,distributed fiber-optic temperature sensors, production logging,injection logging), performing casing integrity check of the selectedwellbore(s), selecting another wellbore to assess for a change intemperature in known water-filled formations, selecting another wellboreto assess a change in light hydrocarbon fractions in known aquifers.

In particular, casing failures occurring at the depths of the sealingformation (intermediate depths) are often the most importantmicroseismic events. They may warrant immediate initiation of remedialprocedures and may lead to disruptions to normal operations.Accordingly, after a casing failure is identified by a microseismicanalysis as described above, the following actions may be taken tofurther confirm if a casing failure occurred: perform a casing integritycheck of the candidate wellbores and/or monitoring the candidatewellbores with additional diagnostic wireline tools. Casing integritychecks may be performed, for example, by using an electromagneticscanner mounted on a production logging tool that detects for anomaliesin the casing. Other methods for checking casing integrity include fluidshots, caliper runs, scraper tests or using DistributedTemperature/Acoustic Sensing via fiber optic cables mounted on a coiledtubing in the monitoring well to detect temperature anomalies. Once acasing failure has been confirmed, the following actions or a subset ofthe following actions may be taken on the identified wellbore(s) if thecasing failure occurred after the reservoir reached high pressure duringthe injection phase: discontinue fluid (steam) injection into theidentified wellbore, shut-in the identified wellbore, inject highdensity fluid in the identified wells to prevent reservoir fluidsentering the formation at the failure interval, or isolate the failureand abandon the identified well. If the casing failure occurred duringthe nitrogen soak phase following a high-pressure injection, theoperator may continue to inject nitrogen or a denser fluid to stabilizethe fluid flow below the failure. If the casing failure occurred duringa low pressure injection cycle, wherein the wellbore pressure at thecasing failure is low enough that the fluids cannot penetrate theoverburden rock, the operator may continue producing the well until theend of the cycle and perform additional casing integrity checks andremediation steps as required prior to the next steaming cycle.

If a microseismic analysis identifies a casing failure within a fewmeters of the reservoir depths, the type of action taken may depend onthe injection pressure in the wellbore at the casing failure. In CSSoperations, for example, if the injection pressure in the wellboreexceeds the fracture pressure of the reservoir rock, the wellbore may beshut-in until additional diagnostics are performed to confirm the casingfailure. On the other hand, if the wellbore pressure was lower than thefracture pressure, the operator may continue injecting steam into thewell at reduced rates to prevent the reservoir reaching fracturepressures until the end of the injection cycle. Once the injection cycleconcludes, further casing integrity checks may be performed to confirmthe casing failure. If the microseismic analysis indicates that theevent is predominantly implosive (e.g., if the Polarity Score is lessthan 0.5), the identified well may nevertheless be identified for futurecasing integrity checks.

In a similar manner, heel events, may also prompt further checks for apotential loss of wellbore integrity. This is especially true if heelevents are detected repeatedly on the same well over time. In general,operational noise, surface noise and high-frequency mechanical noiseevents do not demand any immediate action, and thus the actions takenfor these events are largely up to the discretion of the operator.

In the context of operations, cement cracks may be of interest. Oncecandidate wells with possible multiple cement cracks have beenidentified by the microseismic analysis, further production/injectionlogs, may be performed to evaluate the cement integrity behind theproduction casing, including temperature logs, cement bond logs, noiselogs, etc.

A search for Continuous Microseismic Radiation (CMR) may be conducted:(a) if a series of heave events in the intermediate overburden layershas been recorded or if a known failure exists in a high pressure well,or (b) if a significant DFP alarm is triggered without a seismic event.The CMR search may detect fluid migration into the overburden throughnatural fractures or mechanically weak layers of rock. CMR may occurafter loss of fluid due to a casing break. Depending on the severity ofthe CMR, the identified wells may be shut-in to conduct casing integritychecks to pinpoint the location(s) of the leak. A CMR search may or maynot be accompanied with a temporary cessation of rod pump motion toprovide a quiet monitoring environment.

Shear dominated events such as heaves are usually the result of benignshifts of the formations that result from dilation of the reservoirduring CSS. In rare cases, such events might indicate fluid migrationinto the overburden if there was a breach in the sealing formation. Whenshear dominated events have been identified by microseismic analysisduring operations, the surveillance systems on the candidate wells nearthe event may be enhanced, for example, through additional pressuremonitoring. Several recent monitoring wells have the potential forrunning a distributed fiber optic temperature sensor, which couldprovide additional data on the subsurface conditions. A casing integritycheck may be performed, or the well could be marked for a futureintegrity check. Other possible actions include reducing steam injectionvolumes and/or rates.

Operation Integrity Surveillance Method

With reference to FIG. 14, a method for monitoring operation integrityduring hydrocarbon production or fluid injection operations isillustrated. At step 1402, the method may begin by detectingmicroseismic waves in a subsurface area of operation using a seismicmonitoring system as those described elsewhere herein. The method mayfurther comprise, at step 1404, receiving, from the seismic monitoringsystem, microseismic data representative of the microseismic waves. Themicroseismic data may then be processed, at step 1406, to obtain aplurality of data panels corresponding to microseismic data measuredover a predetermined time interval. For example, the microseismic datamay be divided into data segments of specified length, and the datasegments may be divided into the data panels. In certain embodiments,the data segments may be filtered prior to dividing the data segmentsinto the data panels. In other embodiments, the data panels may overlap.For example, the overlap may be between 10% and 50%. In yet otherembodiments, the predetermined time interval may be between 0.5 and 3seconds.

According to disclosed aspects, a neural network analysis is used todetermine whether any of the data panels, or other subdivision of themicroseismic data, includes a noise event or a non-noise event. For anydata panel or other data subdivision including a non-noise event, themethod disclosed herein may further comprise, at step 1408, calculating,for each data panel, trigger values for data traces corresponding tosensor receivers of the microseismic monitoring system. For example,trigger values may be calculated using an STA/LTA analysis, absoluteamplitude thresholding, relative amplitude thresholding, wavelettransform calculations, or combinations thereof. In some embodimentswhere at least one trigger value is calculated using an STA/LTAanalysis, an STA window of the STA/LTA analysis may be between 5 and 30milliseconds, an LTA window of the STA/LTA analysis may be between 50and 250 milliseconds, and an STA/LTA ratio threshold of the STA/LTAanalysis may be between 2 and 5. In other embodiments where triggervalues are calculated using relative amplitude thresholding, a relativeamplitude ratio of the amplitude thresholding may be between 20 and 50%.

The method may further comprise, at step 1410, selecting, as a triggereddata panel, at least one data panel that satisfies predeterminedtriggering criteria. For example, in some embodiments, the predeterminedtriggering criteria may comprise that the at least one data panel hasoverlapping triggered time windows for data from at least two sensorreceivers. Or the predetermined criteria may comprise that the at leastone data panel has overlapping triggered time windows for data from atleast two sensor receivers and that at least one of the triggered sensorreceivers has at least two triggered channels. Then, at step 1412, themethod may include selecting, as a non-trivial data panel containingmicroseismic data representative of an event, at least one triggereddata panel that satisfies spectral density criteria. For example, thenon-trivial data panel may be selected using a spectral densityselection criteria. In some embodiments, the spectral density criteriamay comprise that the frequency of the 90% cumulative spectral densityof the data panel is below 300 Hz. In other embodiments, the spectraldensity selection criteria may comprise that the frequency of the 90%cumulative spectral density of the data panel is below 300 Hz in awindow of data between 0.1 to 0.5 seconds around a triggered window ofthe data panel.

According to some aspects of the present disclosure, a method describedherein may further comprise, at step 1414, calculating a value for eachof at least two event attributes of a plurality of event attributes ofthe event. In some embodiments, the plurality of event attributescomprises magnitude, proximity, polarity, P/S ratio, and SH/SV ratio.Magnitude may further comprise at least one of peak particle velocity,energy flux, moment flux, and RPPV. Proximity may comprise at least oneof distance between event location and sensor receivers, distancebetween event location and offset wellbores, distance between eventlocation and wellbore intervals, distance between event location andreservoir layers, and distance between event location and naturalfractures or faults. In yet other embodiments, the plurality of eventattributes may further comprise event depth.

In some embodiments, calculating a value for each of at least two eventattributes of a plurality of event attributes may comprise determiningan event location, and using the event location to calculate at leastone of the values. For example, determining an event location maycomprise calculating a set of grid points comprising geometry andacoustic travel time data; rotating the triggered data panel to an Earthcoordinate system; determining an initial set of P-wave arrival timesfor the triggered data panel; calculating a time window about eachP-wave arrival time; calculating an azimuth angle in an horizontal planefor each P-wave arrival time; determining a vertical surface on the setof grid points by estimating a best-fit azimuth angle; calculatingazimuth, inclination, and travel time errors for grid points on thevertical surface; determining a grid point on the vertical surface thathas minimum total error; searching in three dimensions around the gridpoint, including the grid point, for a location with a lowest totalerror; and selecting, as the event location, the location with thelowest total error. In yet other embodiments, the event location may beadjusted by determining one or more S-wave arrival times for thetriggered data panel; recalculating azimuth, inclination, and traveltime errors on the vertical surface; determining a new grid point on thevertical surface that has minimum total error; searching in threedimensions around the new grid point, including the new grid point, fora location with a lowest total error; and selecting, as the eventlocation, the location with the lowest total error. The event locationmay in turn be used to calculate a proximity value by determining atleast one of a distance between event location and sensor receivers, adistance between event location and offset wellbores, distance betweenevent location and wellbore intervals, a distance between event locationand reservoir layers, and distance between event location and naturalfractures or faults.

A method according to the present disclosure may further include, atstep 1416, determining an event score based on the values of theplurality of event attributes. For example, determining an event scoremay comprise calculating a score for each of the at least two eventattributes, and combining the scores for the at least two eventattributes. The score for each of the at least two event attributes maybe indicative of a likelihood that the event occurred based solely onthe value for a given attribute, and an accuracy of said value.Alternatively, determining an event score may comprise calculating amagnitude score, a polarity score, a proximity score, an SH/SV score,and a P/S score; and adding the magnitude score, polarity score,proximity score, SH/SV score, and P/S score to obtain the event score.Next, the method may include, at step 1418, classifying the event intoat least one event category of a plurality of event categories based onthe event score. For example, the plurality of event categories maycomprise casing break, casing slip, CMR event, heel event, heave event,cement crack, surface noise, and rod noise.

FIG. 15 is a flowchart depicting a method of 1500 for determiningwhether a data panel, or other subdivision of the microseismic data,includes a noise event or a non-noise event. Method 1500 may be usedwith the method shown in FIG. 14 as disclosed herein. Method 1500 mayinclude steps for: determining a number of data levels to be used in theneural network analysis (1502), and for each dataset, adjusting a numberof data levels associated with each dataset to match the determinednumber of data levels, wherein each dataset is associated with arespective array of seismic receivers (1504). The adjusting may includeadding data levels to the dataset, the added data levels having neutraldata values or zeroed data values therein. Additionally oralternatively, the adjusting may include discarding data levels in thedataset by calculating, for each trace of each data level, short-time(STSD) and long-time (LTSD) moving standard deviations of data in saideach trace, calculating STSD-to-LTSD ratios (SLR) for each trace,identifying a maximum SLR and a location of the maximum SLR for eachtrace, calculating a frequency threshold for each trace such that apredetermined portion of energy of said each trace is contained belowthe frequency threshold, and discarding a data level if the frequencythreshold is below a cutoff frequency for at least 50% of the traces inthe data level. If, after discarding the data level, the number of theremaining data levels exceeds the determined number of data levels, itis determined which data level of the remaining data levels has a lowestmaximum SLR, and said data level is discarded. This process is repeateduntil the number of the remaining data levels equals the determinednumber of data levels. At block 1506 it may be determined whether amoveout attribute exists for each trace, the moveout attribute being themaximum SLR and its location, and the moveout attribute may be fed intothe neural network analysis. The moveout attribute may also include amaximum STSD value over the standard deviation of said each trace, andthe location of the maximum STSD value. At block 1508, noise spikes maybe identified and fed into the neural network analysis. At block 1510the neural network analysis is run using the above identified inputparameters and according to known principles. At block 1512 the neuralnetwork analysis identifies noise events (i.e., events with noiseattributes) and/or non-noise events (i.e., events with non-noiseattributes). Data panels having including only noise events may bediscarded, and data panels including any non-noise events may be furtherprocessed according to aspects disclosed herein.

Returning to FIG. 14, the method of FIG. 14 optionally may comprisevalidating the event classification using at least one type ofoperational surveillance data (not shown). Operational surveillance datamay include wellhead pressures, injection rates, delta flow-pressurealarms, nitrogen soak trends, wellhead temperature, casing headpressure, casing head temperature, downhole pressure, downholetemperature, injection flow rate, and production flow rate. In certainother embodiments, the method may comprise adjusting one or moreoperation parameters based on the event classification (not shown). Suchoperation parameters may include fluid density, fluid viscosity, fluidcomposition, fluid injection rate, fluid injection pressure, nitrogeninjection rate, and nitrogen injection pressure. Alternatively oradditionally, the method may comprise performing a casing integritycheck if the event category is a casing failure (not shown).

It should be understood that the method illustrated in FIG. 14 is onlyone example of the possible processes and methodologies that may beimplemented according to the disclosure herein. As another example (notillustrated), a method for monitoring operation integrity duringhydrocarbon production or fluid injection operations may comprisedetecting microseismic waves in a subsurface area of operation using aseismic monitoring system; receiving, from the seismic monitoringsystem, microseismic data representative of the microseismic waves;processing the microseismic data to obtain a plurality of data panelscorresponding to microseismic data measured over a predetermined timeinterval; determining, with a neural network analysis, whether an eventis a noise event or a non-noise event; if the event is a non-noiseevent, calculating, for each data panel, trigger values for data tracescorresponding to sensor receivers of the microseismic monitoring system;selecting, as a triggered data panel representative of an event, atleast one data panel that satisfies predetermined triggering criteria;calculating a value for each of at least two event attributes of aplurality of event attributes of the event; and using a classificationalgorithm to classify the event into at least one event category of aplurality of event categories based on the values, as describedelsewhere herein. In some embodiments, using a classification algorithmmay include training the classification algorithm using a trainingdataset of microseismic data corresponding to known event types, andusing the trained classification algorithm and the values for the atleast two event attributes to classify the event. Such classificationalgorithm may be a Decision Tree, a Discriminant Analysis, a SupportVector Machine, a k-Nearest Neighbor Classifier, an Ensemble-basedclassifier, or a Neural Network.

As yet another example (not illustrated), a method for monitoringoperation integrity during hydrocarbon production or fluid injectionoperations may comprise detecting microseismic waves in a subsurfacearea of operation using a seismic monitoring system; receiving, from theseismic monitoring system, microseismic data representative of themicroseismic waves; processing the microseismic data to obtain aplurality of data panels corresponding to microseismic data measuredover a predetermined time interval; determining, with a neural networkanalysis, whether an event is a noise event or a non-noise event; if theevent is a non-noise event, calculating, for each data panel, triggervalues for data traces corresponding to sensor receivers of themicroseismic monitoring system; selecting, as a triggered data panel, atleast one data panel that satisfies predetermined triggering criteria;selecting, as a non-trivial data panel containing microseismic datarepresentative of an event, at least one triggered data panel thatsatisfies spectral density criteria; determining an event location; andusing the event location to classify the event into at least one eventcategory of a plurality of event categories.

Automated Processing

It may be appreciated that the methods disclosed herein may beadvantageously implemented in an automated fashion that providesbenefits in terms of reduced processing time and faster diagnosis ofundesirable conditions, leading to more rapid intervention to addressoperations integrity issues. Leveraging current technology, it ispossible for a casing failure to be detected within a matter of minutesby the methods and systems disclosed herein, whereas prior processeshave required hours at a minimum Automated event processing is also acrucial component in any closed-loop system that can autonomously takeactions based on the event classification. For example, if a casingfailure is detected by the automated event processing system, controllerdevices (either centrally located or distributed at multiple locations)may be programmed to automatically shut-in the candidate well. Themanual method is also dependent on personnel schedules and thussusceptible to associated issues related to time of day and otherfactors. Automated notification of alarms is feasible when the falsepositive alarm rate is low enough such that staff respond to suchnotices as credible alarm conditions, and where the false negative rateis sufficiently low such that the system is trustworthy. Such noticesinclude but are not limited to email, text message, instant messaging,phone call, and other similar communication methods.

In this regard, a more effective system with rapid turnaround andresponse provides substantial benefits to operational integrity andenvironmental performance. For example, a more rapid response to acasing failure can lead to a reduced volume of fluid incursion into theoverburden.

System Implementation

All methods and processes described herein may be implemented byconventional computer systems. For example, such a computer system mayinclude a central processing unit (CPU) coupled to a system bus. The CPUmay be any general-purpose CPU, although other types of CPUarchitectures (or other components) may be used that support theoperations described herein. Those of ordinary skill in the art willappreciate that, while only a single CPU may be sufficient, additionalCPUs may be present in the computer system. Moreover, the computersystem may comprise a networked, multi-processor computer system thatmay include a hybrid parallel CPU/GPU system. The CPU may executevarious logical instructions according to teachings disclosed herein.For example, the CPU may execute machine-level instructions forperforming processing according to the methods described above.

Computer systems contemplated herein that may implement the disclosedteachings may include computer components such as non-transitorycomputer-readable media. Examples of computer-readable media includerandom access memory (RAM), which may be SRAM, DRAM, SDRAM, or the like.The computer system may also include additional non-transitory,computer-readable media such as read-only memory (ROM), which may bePROM, EPROM, EEPROM, or the like. RAM and ROM may store user and systemdata and programs, as is known in the art. The computer system may alsoinclude one or more input/output (I/O) adapters, communicationsadapters, graphics processing units, user interface adapters, displaydrivers, and display adapters.

The I/O adapter may connect additional non-transitory, computer-readablemedia such as one or more storage devices, including, for example, ahard drive, a compact disc (CD) drive, a floppy disk drive, a tapedrive, and the like, to the computer system. Such storage device(s) maybe used when RAM is insufficient for the memory requirements associatedwith storing data for implementations of the present techniques. Thedata storage of the computer system may be used for storing informationand/or other data used or generated as disclosed herein, includingmicroseismic data. In some embodiments, such a network mayadvantageously utilize remote “cloud” storage systems and othernetworked systems. Storage device(s) may also be used to storealgorithms or software designed to implement the teachings herein.Further, one or more user interface adapters may couple user inputdevices, such as a keyboard, a pointing device, and/or output devices,to the computer system. A display adapter may be driven by the CPU tocontrol a display driver and a display on a display device, for example,to present microseismic data and information generated throughapplication of the microseismic analyses of the present disclosure.

The architecture of a computer system suitable to implement methodsdescribed above may be varied as desired. For example, any suitableprocessor-based device may be used, including without limitations,personal computers, laptop computers, computer workstations, andmulti-processor servers. Moreover, the present technologicaladvancements may be implemented on application specific integratedcircuits (ASICs) or very large scale integrated (VLSI) circuits. Infact, persons of ordinary skill in the art may use any number ofsuitable hardware structures capable of executing logical operationsaccording to the present technological advancement. Input data to thecomputer system may include various plug-ins and library files. Inputdata may additionally include configuration information.

The above examples of methods that may be implemented according to thepresent disclosure to monitor operation integrity during hydrocarbonproduction or fluid injection operations will be apparent to thoseskilled in the art. For example, no automated systems exist today thatare able to identify and classify events of interest on the basis oftheir particular microseismic signature. Whether by manual programmingaccording to examples provided herein, or machine-learning algorithms,the noise-filtered, point-based classification processes disclosedherein provide significant advantages to monitoring operations,including lower costs and increased efficiency, and increased operationsintegrity, safety, and environmental performance.

It should be understood that the numerous changes, modifications, andalternatives to the preceding disclosure can be made without departingfrom the scope of the disclosure. The preceding description, therefore,is not meant to limit the scope of the disclosure. Rather, the scope ofthe disclosure is to be determined only by the appended claims and theirequivalents. It is also contemplated that structures and features in thepresent examples can be altered, rearranged, substituted, deleted,duplicated, combined, or added to each other.

What is claimed is:
 1. A method for monitoring operation integrityduring hydrocarbon production or fluid injection operations usingmicroseismic data, comprising: detecting microseismic waves in asubsurface area of operation using a seismic monitoring system;receiving, from the seismic monitoring system, microseismic datarepresentative of the microseismic waves; processing, with a computer,the microseismic data to obtain a plurality of data panels correspondingto microseismic data measured over a predetermined time interval;determining, with a neural network analysis implemented on the computer,whether any of the plurality of data panels includes a noise event or anon-noise event; for data panels including a non-noise event,calculating with the computer, for each data panel, trigger values fordata traces corresponding to sensor receivers of the microseismicmonitoring system; selecting with the computer, as a triggered datapanel, at least one data panel that satisfies predetermined triggeringcriteria; selecting with the computer, as a non-trivial data panelcontaining microseismic data representative of an event, at least onetriggered data panel that satisfies spectral density criteria;calculating, with the computer, a value for each of at least two eventattributes of a plurality of event attributes of the event; determining,with the computer, an event score based on the values of the pluralityof event attributes; and classifying, with the computer, the event intoat least one event category of a plurality of event categories based onthe event score.
 2. The method of claim 1, wherein determining whetherany of the plurality of data panels includes a noise event or anon-noise event comprises: determining a number of data levels to beused in the neural network analysis; and for each dataset, adjusting anumber of data levels associated with each dataset to match thedetermined number of data levels; wherein each dataset is associatedwith a respective array of seismic receivers.
 3. The method of claim 2,wherein the adjusting comprises: adding data levels to the dataset, theadded data levels having neutral data values or zeroed data valuestherein.
 4. The method of claim 2, wherein the adjusting comprisesdiscarding data levels in the dataset by calculating, for each trace ofeach data level, short-time (STSD) and long-time (LTSD) moving standarddeviations of data in said each trace; calculating, for each trace,STSD-to-LTSD ratios (SLR); for each trace, identifying a maximum SLR anda location of the maximum SLR; calculating, for each trace, a frequencythreshold such that a predetermined portion of energy of said each traceis contained below the frequency threshold; and discarding a data levelif the frequency threshold is below a cutoff frequency for at least 50%of the traces in the data level.
 5. The method of claim 4, wherein thediscarded data level is a first discarded data level and data levels notdiscarded are remaining data levels, and further comprising: if, afterdiscarding the first discarded data level, a number of the remainingdata levels exceeds the determined number of data levels, (a)determining which data level of the remaining data levels has a lowestmaximum SLR; (b) discarding the data level with the lowest maximum SLR;and (c) repeating steps (a) and (b) until the number of the remainingdata levels equals the determined number of data levels.
 6. The methodof claim 2, further comprising: using a computer, determining whether amoveout attribute exists for each trace, wherein the moveout attributecomprises the maximum SLR and the location of the maximum SLR; andfeeding the moveout attribute into the neural network analysis.
 7. Themethod of claim 6, wherein the moveout attribute further comprises, foreach trace: a maximum STSD value over the standard deviation of saideach trace; and a location of the maximum STSD value.
 8. The method ofclaim 2, further comprising: identifying noise spikes; and feeding theidentified noise spikes into the neural network analysis.
 9. A methodfor monitoring operation integrity during hydrocarbon production orfluid injection operations, comprising: detecting microseismic waves ina subsurface area of operation using a seismic monitoring system;receiving, from the seismic monitoring system, microseismic datarepresentative of the microseismic waves; processing, with a computer,the microseismic data to obtain a plurality of data panels correspondingto microseismic data measured over a predetermined time interval;determining, with a neural network analysis implemented on the computer,whether any of the plurality of data panels includes a noise event or anon-noise event; for data panels including a non-noise event,calculating with the computer, for each data panel, trigger values fordata traces corresponding to sensor receivers of the microseismicmonitoring system; selecting with the computer, as a triggered datapanel representative of an event, at least one data panel that satisfiespredetermined triggering criteria; calculating, with the computer, avalue for each of at least two event attributes of a plurality of eventattributes of the event; and using, with the computer, a classificationalgorithm to classify the event into at least one event category of aplurality of event categories based on the values.
 10. A method formonitoring operation integrity during hydrocarbon production or fluidinjection operations, comprising: detecting microseismic waves in asubsurface area of operation using a seismic monitoring system;receiving, from the seismic monitoring system, microseismic datarepresentative of the microseismic waves; processing, with a computer,the microseismic data to obtain a plurality of data panels correspondingto microseismic data measured over a predetermined time interval;determining, with a neural network analysis implemented on the computer,whether any of the plurality of data panels includes a noise event or anon-noise event; for data panels including a non-noise event,calculating with the computer, for each data panel, trigger values fordata traces corresponding to sensor receivers of the microseismicmonitoring system; selecting with the computer, as a triggered datapanel, at least one data panel that satisfies predetermined triggeringcriteria; selecting with the computer, as a non-trivial data panelcontaining microseismic data representative of an event, at least onetriggered data panel that satisfies spectral density criteria;determining, with the computer, an event location; and using, with thecomputer, the event location to classify the event into at least oneevent category of a plurality of event categories.
 11. A non-transitorycomputer usable medium having a computer readable program code embodiedtherein, said computer readable program code adapted to be executed by acomputer to implement a method for monitoring operation integrity duringhydrocarbon production or fluid injection operations using microseismicdata, said method comprising: receiving microseismic data representativeof microseismic waves in a subsurface area of operation; processing themicroseismic data to obtain a plurality of data panels corresponding tomicroseismic data measured over a predetermined time interval;determining, with a neural network analysis, whether any of theplurality of data panels includes a noise event or a non-noise event;for data panels including a non-noise event, calculating, for each datapanel, trigger values for data traces corresponding to sensor receiversof the microseismic monitoring system; selecting, as a triggered datapanel, at least one data panel that satisfies predetermined triggeringcriteria; selecting, as a non-trivial data panel containing microseismicdata representative of an event, at least one triggered data panel thatsatisfies spectral density criteria; calculating a value for each of atleast two event attributes of a plurality of event attributes of theevent; determining an even score based on the values of the plurality ofevent attributes; and classifying the event into at least one eventcategory of a plurality of event categories based on the event score.12. The non-transitory computer usable medium of claim 11, wherein thecomputer readable program code further includes code to implement themethod for monitoring operation integrity wherein determining whetherany of the plurality of data panels includes a noise event or anon-noise event comprises: determining a number of data levels to beused in the neural network analysis; and for each dataset, adjusting anumber of data levels associated with each dataset to match thedetermined number of data levels; wherein each dataset is associatedwith a respective array of seismic receivers.
 13. The non-transitorycomputer usable medium of claim 12, wherein the computer readableprogram code further includes code to implement the method formonitoring operation integrity wherein the adjusting comprises: addingdata levels to the dataset, the added data levels having neutral datavalues or zeroed data values therein.
 14. The non-transitory computerusable medium of claim 12, wherein the computer readable program codefurther includes code to implement the method for monitoring operationintegrity wherein the adjusting comprises discarding data levels in thedataset by calculating, for each trace of each data level, short-time(STSD) and long-time (LTSD) moving standard deviations of data in saideach trace; calculating, for each trace, STSD-to-LTSD ratios (SLR); foreach trace, identifying a maximum SLR and a location of the maximum SLR;calculating, for each trace, a frequency threshold such that apredetermined portion of energy of said each trace is contained belowthe frequency threshold; and discarding a data level if the frequencythreshold is below a cutoff frequency for at least 50% of the traces inthe data level.
 15. The non-transitory computer usable medium of claim14, wherein the computer readable program code further includes code toimplement the method for monitoring operation integrity wherein thediscarded data level is a first discarded data level and data levels notdiscarded are remaining data levels, and further comprising: if, afterdiscarding the first discarded data level, a number of the remainingdata levels exceeds the determined number of data levels, (a)determining which data level of the remaining data levels has a lowestmaximum SLR; (b) discarding the data level with the lowest maximum SLR;and (c) repeating steps (a) and (b) until the number of the remainingdata levels equals the determined number of data levels.
 16. Thenon-transitory computer usable medium of claim 12, wherein the computerreadable program code further includes code to implement the method formonitoring operation integrity, further comprising: using a computer,determining whether a moveout attribute exists for each trace, whereinthe moveout attribute comprises the maximum SLR and the location of themaximum SLR; and feeding the moveout attribute into the neural networkanalysis.
 17. The non-transitory computer usable medium of claim 16,wherein the computer readable program code further includes code toimplement the method for monitoring operation integrity wherein themoveout attribute further comprises, for each trace: a maximum STSDvalue over the standard deviation of said each trace; and a location ofthe maximum STSD value.
 18. The non-transitory computer usable medium ofclaim 12, wherein the computer readable program code further includescode to implement the method for monitoring operation integrity furthercomprising: identifying noise spikes; and feeding the identified noisespikes into the neural network analysis.
 19. A method of identifying anevent during hydrocarbon production or fluid injection operations usingmicroseismic data, comprising: detecting microseismic waves in asubsurface area of operation using a seismic monitoring system;receiving, from the seismic monitoring system, microseismic datarepresentative of the microseismic waves; determining, with a neuralnetwork analysis implemented on a computer, whether any portion of themicroseismic data includes a noise event or a non-noise event; and forportions of the microseismic data including a non-noise event,classifying, with the computer, the event into at least one eventcategory of a plurality of event categories; wherein determining whetherany portion of the microseismic data includes a noise event or anon-noise event comprises: determining a number of data levels to beused in the neural network analysis; and for each dataset, adjusting anumber of data levels associated with each dataset to match thedetermined number of data levels; each dataset being associated with arespective array of seismic receivers; using the computer, determiningwhether a moveout attribute exists for each trace, wherein the moveoutattribute comprises the maximum SLR and the location of the maximum SLR;using the computer, identifying noise spikes; and feeding the moveoutattribute and the identified noise spikes into the neural networkanalysis.
 20. The method of claim 19, wherein the adjusting comprises:adding data levels to the dataset, the added data levels having neutraldata values or zeroed data values therein.
 21. The method of claim 19,wherein the adjusting comprises discarding data levels in the dataset bycalculating, for each trace of each data level, short-time (STSD) andlong-time (LTSD) moving standard deviations of data in said each trace;calculating, for each trace, STSD-to-LTSD ratios (SLR); for each trace,identifying a maximum SLR and a location of the maximum SLR;calculating, for each trace, a frequency threshold such that apredetermined portion of energy of said each trace is contained belowthe frequency threshold; and discarding a first data level if thefrequency threshold is below a cutoff frequency for at least 50% of thetraces in the data level, wherein data levels not discarded areremaining data levels; if, after discarding the first discarded datalevel, a number of the remaining data levels exceeds the determinednumber of data levels, (a) determining which data level of the remainingdata levels has a lowest maximum SLR; (b) discarding the data level withthe lowest maximum SLR; and (c) repeating steps (a) and (b) until thenumber of the remaining data levels equals the determined number of datalevels.
 22. The method of claim 21, wherein the moveout attributefurther comprises, for each trace: a maximum STSD value over thestandard deviation of said each trace; and a location of the maximumSTSD value.